GPU for LLM Inference: Which Graphics Card to Rent for 7B/70B Models

For efficient LLM inference of 7B models, a graphics card with 8-12 GB of VRAM is sufficient (e.g., RTX 3060/3080/4060/4070 for 4-bit quantization), while 70B models will require a minimum of 24-48 GB of VRAM (RTX 4090, A6000, A100, or H100) depending on the chosen quantization level and bandwidth requirements.

Which GPU to Rent for LLM Inference: VRAM Calculation and Basic Principles

Choosing the optimal graphics processing unit (GPU) for large language model (LLM) inference is a critically important step that determines both the performance and cost of your operations. The main factor to consider when selecting a GPU for LLM is the amount of video memory (VRAM). VRAM specifically determines which model you can load and with what degree of quantization.

VRAM Calculation: How Much Video Memory Do You Need for LLM?

The amount of VRAM required to load an LLM directly depends on two key parameters:

1. Number of Model Parameters: The more parameters, the more VRAM is required. Models are measured in billions (B) of parameters.
2. Parameter Representation Precision (Quantization): This is the number of bits used to store each parameter.

The basic formula for calculating VRAM is as follows:

VRAM_consumption = (Number_of_parameters * Parameter_size_in_bytes) + Buffers_and_activations

Let's consider standard scenarios:

• FP32 (Full Precision): 4 bytes per parameter. This is the maximum precision, requiring the largest amount of VRAM.
• FP16/BF16 (Half Precision): 2 bytes per parameter. Standard for most modern LLMs, significantly reduces VRAM compared to FP32.
• INT8 (8-bit Quantization): 1 byte per parameter. A popular method for inference, greatly reducing VRAM with minimal quality loss.
• INT4 (4-bit Quantization): 0.5 bytes per parameter. Maximum VRAM savings, but may lead to a noticeable decrease in generation quality and requires specialized libraries.

Beyond the model itself, VRAM is also used to store buffers, activations, key-value (KV) cache for context, and for the system needs of frameworks (PyTorch, TensorFlow). Typically, these additional overheads range from 10% to 30% of the model size, but can be higher for very long contexts.

Approximate calculation for a model with 7 billion parameters (7B) and 70 billion parameters (70B):

As seen from the table, even for a 7B model in FP16, you will need a GPU with 16 GB of VRAM or more. For a 70B model in INT4, you will need a minimum of 42 GB, which already requires professional cards like the A100 or H100.

LLM Quantization: How to Save VRAM and What Methods Exist

Quantization is the process of reducing the precision of the numerical representation of model parameters, which significantly reduces the required VRAM and often speeds up inference. This approach has become a cornerstone for deploying large LLMs on less powerful hardware or for reducing the cost of cloud GPU resources. Understanding how much VRAM is needed for LLM is directly related to the chosen quantization method.

Quantization Methods: QLoRA, GPTQ, AWQ, and Others

There are several popular quantization methods, each with its own features, advantages, and disadvantages:

1. BitsAndBytes (BNB): One of the simplest and most widely used methods, especially for 8-bit and 4-bit quantization. It is integrated into Hugging Face transformers and allows loading models in a quantized form with minimal effort. BNB 4-bit is often used in combination with QLoRA for model tuning.
2. GPTQ (GPT-Q): This is a Post-Training Quantization (PTQ) method that allows quantizing a model down to 4-bit without significant quality loss. GPTQ is optimized for inference and often provides better performance compared to BNB at the same degree of quantization. Specialized libraries, such as AutoGPTQ, are required to work with GPTQ.
3. AWQ (Activation-aware Weight Quantization): A relatively new PTQ method that focuses on quantizing weights sensitive to activations. AWQ often surpasses GPTQ in quality for 4-bit quantization, especially for certain types of models, and also demonstrates high inference speed.
4. QLoRA (Quantized LoRA): Although QLoRA itself is not a quantization method for full model inference, it allows for fine-tuning LLMs loaded in a 4-bit quantized form. The base model remains in 4-bits, while small LoRA adapters are trained in higher precision, significantly reducing VRAM requirements for training. After training, LoRA adapters can be "merged" with the base model or used separately for inference.

Example of loading a 4-bit model using transformers and BitsAndBytes:

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

model_id = "mistralai/Mistral-7B-Instruct-v0.2"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto"
)

print(f"Model loaded on {model.device} with {model.get_memory_footprint() / (1024**3):.2f} GB VRAM")
# Approximately 4.5 - 5.5 GB VRAM for a 7B model

Impact of Quantization on Quality and Speed

Quantization is not without cost. It involves trade-offs:

• Generation Quality: When transitioning from FP16 to INT8, quality losses are usually minimal and often imperceptible for most tasks. However, 4-bit quantization can lead to a more noticeable reduction in quality, especially for complex tasks requiring high precision or for models that are particularly sensitive to information loss (e.g., models for code generation or mathematical calculations). It is important to test the quantized model on your specific tasks.
• Inference Speed: Quantization, especially down to 4-bit, often leads to faster inference. A smaller data volume to process means faster data transfer over the memory bus and more efficient GPU cache utilization. However, this advantage can be negated if unoptimized kernels are used for quantized operations or if the overhead of de-quantization/re-quantization becomes too high. Modern libraries and GPU architectures (e.g., NVIDIA Tensor Cores) have specialized support for low-bit operations, making inference significantly faster.

The choice of quantization method and its degree depends on your priorities: maximum VRAM savings and speed with acceptable quality reduction, or maintaining maximum quality with higher VRAM requirements.

Popular GPUs for LLM Inference: From Consumer to Server-Grade

A wide range of GPUs capable of performing LLM inference exists on the market. The choice of GPU for LLM depends on budget, required VRAM, performance, and availability. Let's look at the most popular options.

Consumer GPUs: RTX 4090 and Equivalents

NVIDIA GeForce RTX series consumer graphics cards (e.g., RTX 30xx, RTX 40xx) are an excellent choice for local inference and for small cloud projects where cost is a critical factor. Their main advantage is a relatively low price per gigabyte of VRAM and high performance for their money.

• NVIDIA GeForce RTX 4090 (24 GB VRAM): The flagship of the consumer segment. It features 24 GB of VRAM, allowing it to run 7B models in FP16, as well as 70B models in INT4 (and sometimes even 8-bit with some tricks). Thanks to the Ada Lovelace architecture and Tensor Cores, the RTX 4090 delivers impressive inference performance. It is one of the best cards in terms of price/performance ratio for individual developers and small teams.
• NVIDIA GeForce RTX 3090/3090 Ti (24 GB VRAM): The previous generation of flagships. They also have 24 GB of VRAM and can handle the same tasks as the RTX 4090, albeit at a lower speed. They can be a more budget-friendly option on the secondary market or in some cloud offerings.
• NVIDIA GeForce RTX 4080/4070 Ti/3080/3070 (12-16 GB VRAM): These cards are suitable for 7B models in FP16 or for larger models (up to 13B/20B) in highly quantized form (INT4). Their VRAM is limited, but they are sufficient for many tasks and more accessible.

Features of Consumer GPUs:

• VRAM: Maximum 24 GB per card. Very large models may require a multi-GPU configuration, which complicates model distribution and synchronization.
• Interconnect: Lack of NVLink (except for RTX 3090/3090 Ti), which limits data exchange speed between GPUs in multi-card systems.
• Availability: Easier to find and rent on platforms focused on consumer GPUs.

Server/Data Center GPUs: A100, H100, A6000

Professional GPUs are designed for high-performance computing in data centers. They offer significantly more VRAM, high memory bandwidth, improved interconnect (NVLink), and more reliable 24/7 operation.

• NVIDIA A100 (40 GB or 80 GB VRAM): A workhorse for AI inference and training. Versions with 40 GB and 80 GB of VRAM exist. The 80 GB version allows seamlessly running 70B models in INT8 (consuming ~84 GB VRAM) on a single card or in FP16 by distributing across two A100 80GB cards. The A100 features high memory bandwidth and specialized Tensor Cores for AI operations.
• NVIDIA H100 (80 GB VRAM): NVIDIA's flagship solution, succeeding the A100. It boasts a significantly improved Hopper architecture, providing several times greater performance for AI tasks compared to the A100, especially in FP8 and FP16. The H100 80GB is an ideal choice for the most demanding 70B models in INT8 or even some in FP16, as well as for multi-GPU inference of huge models. Renting an H100 will, of course, be more expensive. How much does H100 rental cost in the cloud is a question requiring separate analysis, but Valebyte.com offers competitive prices.
• NVIDIA RTX A6000 (48 GB VRAM): This is a professional card based on the Ampere architecture (like the A100), but with 48 GB of VRAM. It occupies an intermediate position between consumer RTX cards and server-grade A100/H100 cards. The A6000 48GB is excellent for 70B models in INT4 and some 8-bit scenarios. It is often available at a lower price than the A100 while offering a significant amount of VRAM.

Features of Server GPUs:

• VRAM: Up to 80 GB per card, allowing very large models to be run.
• Interconnect: NVLink support for high-speed connection between GPUs, which is critically important for multi-GPU inference and training.
• Reliability and Support: Designed for continuous operation in data centers, with extended warranty and support.
• Cost: Significantly higher to purchase, but when rented, they can be more cost-effective for high-load tasks due to their performance.

GPU Comparison: RTX 4090, A100, H100 – Which to Choose for LLM Inference?

Choosing the optimal inference GPU is a balance between VRAM, performance, cost, and availability. Let's compare three key players in detail: NVIDIA RTX 4090 (consumer flagship), NVIDIA A100 (proven server workhorse), and NVIDIA H100 (new server leader).

Table Comparing Key GPU Characteristics for LLM

This table will help you quickly navigate the capabilities of each GPU in terms of LLM inference.

Note: Rental prices are approximate and can vary significantly depending on the provider, region, rental duration, and availability.

Performance in LLM Inference: What 4090/A100/H100 Can Handle

GPU performance for LLM inference is measured in tokens per second (tokens/sec) and depends on many factors: model size, quantization level, context length, batch size, and framework optimizations.

• NVIDIA RTX 4090:
  • 7B model (FP16): Handles it excellently, delivering tens of tokens per second even with long contexts. Definitely the best choice for a GPU for a 7B model.
  • 70B model (INT4): Can be run, but with compromises. Performance will be lower than on A100/H100, and with long contexts, it may feel "choked" due to lower memory bandwidth compared to HBM.
• NVIDIA A100 (80GB):
  • 7B model (FP16/BF16): Overkill, but provides very high speed.
  • 70B model (INT8): A standard and very efficient scenario. An A100 80GB can easily load a 70B model in INT8 (consuming ~84 GB VRAM) and provide high throughput. For FP16, two such cards would be required.
  • 130B+ models (INT4): Excellent for such models in quantized form.
• NVIDIA H100 (80GB):
  • 7B/70B model (FP16/BF16): Maximum performance. The H100 provides a significant speed boost compared to the A100, especially for FP16 and FP8 operations, thanks to more powerful Tensor Cores and HBM3 memory. This is the ideal GPU for a 70B model, especially if minimal latency or maximum throughput is required.
  • Large models (175B+, 300B+): The H100, especially in multi-GPU configurations with NVLink, allows running and inferring huge models that were previously only available on clusters.

It is important to note that actual performance also depends on software optimization (e.g., using Triton kernels, FlashAttention). Achieving maximum inference speed often requires specialized frameworks such as vLLM, TensorRT-LLM, or llama.cpp.

Throughput vs. Latency in LLM Inference

When deploying LLMs for inference, it is crucial to understand the difference between throughput and latency, and how these metrics influence GPU selection and optimization strategy.

What are Throughput and Latency?

• Latency: This is the time it takes for the model to generate the first token (Time To First Token, TTFT) or to generate the entire response to a single request. Low latency is critically important for interactive applications, chatbots, where the user expects an instant response.
• Throughput: This is the number of tokens or requests that the model can process per unit of time (e.g., tokens/second or requests/second). High throughput is important for batch processing, API services with a large number of concurrent users, or for tasks where many independent requests need to be processed quickly.

Optimization for Different Scenarios

The choice between optimizing for throughput or latency affects GPU configuration and inference approaches:

1. Low Latency Scenarios:
   • Examples: Real-time chatbots, interactive assistants, code auto-completion.
   • Requirements: The user expects an immediate response. A delay of several hundred milliseconds can already be noticeable.
   • Optimization:
     • Small Batch Size: Often 1 or very small to minimize waiting time for other requests.
     • Powerful GPUs: GPUs with high clock speeds and optimized cores (e.g., H100) are used for fast computation of each token.
     • Specialized Frameworks: vLLM, TensorRT-LLM, FlashAttention for accelerating computations and KV-cache management.
     • Minimal Quantization: If quality allows, 4-bit quantization can speed up inference by reducing data volume.
2. High Throughput Scenarios:
   • Examples: Analyzing large volumes of text, generating content for SEO, scheduled request processing, APIs for mass use.
   • Requirements: Ability to process as many requests as possible per unit of time, even if individual requests might have slightly higher latency.
   • Optimization:
     • Large Batch Size: Combining multiple requests into a single batch for parallel processing. This increases GPU utilization but also increases latency for each individual request in the batch.
     • Dynamic Batching Techniques: For example, Continuous Batching in vLLM, which allows efficient GPU utilization by processing requests as they arrive and combining them into dynamic batches.
     • Optimal VRAM Usage: Quantization (8-bit, 4-bit) allows loading more models or using larger batches on a single GPU.
     • Multiple GPUs: Distributing the load across several GPUs to scale throughput.

It is important to remember that latency and throughput are often inversely related metrics: optimizing one often leads to a compromise with the other. Modern frameworks, such as vLLM, try to strike a balance by using techniques like PagedAttention for efficient KV-cache management and improving both metrics.

Specific Recommendations: Which GPU to Rent for 7B/70B Models

The choice of a specific GPU for a 70B model or a 7B model depends on your budget, performance requirements, and willingness to compromise on quality through quantization.

Recommendations for 7B Models

Models with 7 billion parameters (7B) are among the most popular for local deployment and many cloud applications due to their good balance between performance and resource requirements.

• 7B in FP16/BF16 (consumption ~16-18 GB VRAM):
  • Recommended GPUs: NVIDIA RTX 4090 (24 GB), NVIDIA RTX A4000/A5000 (16/24 GB), NVIDIA RTX 3090/3090 Ti (24 GB).
  • Comment: The RTX 4090 is an ideal choice. It not only has sufficient VRAM but also provides very high inference speed. If the budget is limited, the RTX 3090 or A5000 will also suffice.
• 7B in INT8 (consumption ~8-10 GB VRAM):
  • Recommended GPUs: NVIDIA RTX 3060 (12 GB), RTX 4060 Ti (16 GB), RTX 3070/3080/4070 (8-12 GB), as well as any cards recommended for FP16.
  • Comment: Even more budget-friendly cards are suitable for INT8 quantization. The RTX 3060 12GB is an excellent option for price/VRAM ratio. The RTX 4060 Ti with 16 GB VRAM is also a good choice, offering a modern architecture.
• 7B in INT4 (consumption ~4-6 GB VRAM):
  • Recommended GPUs: Virtually any modern GPU with 6 GB VRAM or more (e.g., RTX 3050, GTX 1080 Ti, RTX 2060).
  • Comment: INT4 allows running 7B models on very modest hardware. However, it's always worth checking the generation quality, as 4-bit quantization can be noticeable.

Recommendations for 70B Models

Models with 70 billion parameters (70B) are significantly more demanding in terms of VRAM and computational power. They often require server-grade GPUs or multi-GPU configurations.

• 70B in FP16/BF16 (consumption ~160-180 GB VRAM):
  • Recommended GPUs: Minimum 2x NVIDIA A100 80GB or 2x NVIDIA H100 80GB in an NVLink configuration.
  • Comment: This scenario is for the most demanding applications where FP16 quality is critically important. Renting such capacities will be the most expensive.
• 70B in INT8 (consumption ~80-90 GB VRAM):
  • Recommended GPUs: NVIDIA A100 80GB, NVIDIA H100 80GB, or 2x NVIDIA RTX A6000 48GB.
  • Comment: The A100 80GB or H100 80GB are optimal choices for 70B INT8. They provide sufficient VRAM on a single card and high performance. Two A6000 48GB cards will also work, but may be more complex to set up due to the need to distribute the model across two GPUs.
• 70B in INT4 (consumption ~40-50 GB VRAM):
  • Recommended GPUs: NVIDIA RTX A6000 (48 GB), NVIDIA A100 (40 GB or 80 GB), NVIDIA H100 (80 GB), or 2x NVIDIA RTX 4090 (24 GB).
  • Comment: The RTX A6000 48GB is an excellent option in terms of price/performance for this scenario. It allows running a 70B INT4 model on a single card. Two RTX 4090 cards can also be used, but will require model distribution and may have lower inter-GPU bandwidth compared to NVLink.

When choosing a GPU for LLM inference, always start with the least resource-intensive option (maximum quantization) and increase requirements if quality becomes unacceptable.

Where to Rent a GPU and What to Look For

Renting a GPU in the cloud or from specialized providers is the most flexible and cost-effective solution for most LLM inference projects, especially for 70B models and above. Purchasing expensive hardware is only justified for very high and constant workloads.

Cost and Availability

• Hourly Billing: Most cloud providers offer hourly billing, which is ideal for experiments, development, and tasks with variable loads.
• Long-term Contracts: For stable, high-load services, long-term contracts (monthly, yearly) with discounts can be advantageous.
• Provider Comparison: Prices for the same GPUs can vary significantly. Compare offers from major cloud giants (AWS, GCP, Azure) and specialized GPU hosts (Valebyte.com, RunPod, Vast.ai, Lambda Labs). Vast.ai vs RunPod vs Lambda: where to rent a GPU cheaper in 2026 is an excellent resource for such a comparison.
• GPU Availability: Some GPUs, especially H100, may be in short supply, and their availability varies by region and provider.

Infrastructure and Support

When choosing a GPU rental provider, in addition to cost, pay attention to the following factors:

1. GPU Type: Ensure that the provider offers the specific GPU models you need (RTX 4090, A100, H100, A6000).
2. VRAM Capacity: Check that the available GPU configurations have sufficient VRAM for your model and quantization level.
3. Interconnect Speed (NVLink): If you plan to use multi-GPU configurations for very large models, ensure that the GPUs are connected via NVLink for high bandwidth.
4. Processor (CPU) and RAM: Although the GPU is the primary resource, a sufficiently powerful CPU and ample random access memory (RAM) are also important for overall system performance, especially for loading the model and processing data before and after inference. A minimum of 2-4 vCPUs and 16-32 GB of RAM is recommended for most LLM tasks.
5. Disk Subsystem: A fast NVMe SSD is critically important for quickly loading large models and working with files.
6. Network Bandwidth: If your inference service will handle many external requests, high network bandwidth is important.
7. Images and Tools: The availability of ready-to-use Docker images with pre-installed NVIDIA drivers, CUDA, PyTorch/TensorFlow, as well as specialized frameworks (vLLM, TensorRT-LLM) significantly simplifies deployment.
8. Support: Quality technical support can save you a lot of time and frustration when problems arise.
9. Data Center Location: Choose data centers located closer to your target audience or data sources to minimize latency.

Valebyte.com offers a wide selection of dedicated servers with GPUs and VPS with GPUs, allowing you to choose the optimal solution for any LLM inference task, while ensuring high performance and competitive prices. We understand that every project is unique and are ready to assist in selecting a configuration that will meet your VRAM, speed, and budget requirements.

For example, if you need reliable infrastructure for stable operation, Valebyte offers Vultr alternatives with hourly billing, including GPU servers.

Conclusion

The choice of GPU for LLM inference of 7B/70B models is determined by VRAM capacity, quantization level, and performance requirements. For 7B models in 4-bit quantization, 8-12 GB of VRAM is sufficient (RTX 3060/4060 Ti), whereas 70B models in INT4 will require 48 GB of VRAM (RTX A6000) or 80 GB (A100/H100) for INT8, and for FP16 — several H100s. When renting a GPU, it's important to consider not only the cost but also availability, provider infrastructure, and the presence of NVLink for multi-GPU configurations.