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Choose VRAM for the complete inference workload, not for the size of the model download. A local LLM needs memory for its weights, the context and KV cache, and runtime overhead. An 8GB card is a sensible starting point for a quantized 4B-class model; 12–16GB suits roughly 9B–12B models; and 24GB or more opens the door to 27B-class models when the selected quantization and context fit. Larger models can run with CPU offload or multiple GPUs, but they will be slower than a model that fits entirely in GPU memory.
This guide gives you a sizing method, explains the 7B/13B/70B question, and shows how to choose between GPUs when more than one card can run the same model.
What GPU memory does local LLM inference actually use?
Inference memory has three main components:
- Model weights: the parameters loaded from the model file. Quantization changes how many bits represent those parameters.
- Context and KV cache: memory used for your prompt, conversation history, retrieved documents, tool output, and generated tokens. A larger context window increases this allocation.
- Runtime and system overhead: the inference engine, temporary buffers, CUDA or other backend allocations, the operating system, and other applications using the card.
NVIDIA’s PAIR documentation states that “a model’s download size is not its total memory requirement.” A file that appears to fit in available VRAM can still fail when the runtime allocates the context cache and working buffers. Discrete GPU VRAM and system RAM are separate resources: free system RAM does not automatically become free VRAM.
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A practical VRAM tier guide
NVIDIA’s RTX local-LLM guidance provides useful starting tiers. These are selection guidelines rather than guarantees for every model, quantization, context length, or backend.
| Usable GPU VRAM | Reasonable starting target | What to watch |
|---|---|---|
| 6–8GB | Qwen 3.5 4B or another 4B-class quantized model | Keep context moderate and reserve room for runtime allocations. |
| 12–16GB | Qwen 3.5 9B or Gemma 4 12B | Long prompts and higher-precision formats can consume the remaining headroom. |
| 24GB or more | Qwen 3.6 27B, provided the chosen quantization and context fit | Confirm the actual model format, KV-cache settings, and backend support. |
The general rule from NVIDIA is to use the most powerful model that fits comfortably in GPU memory. “Comfortably” means enough capacity remains for the context you intend to use and the normal overhead of your operating system and inference application.
How much VRAM do 7B, 13B, and 70B models need?
Parameter counts are useful for comparing model scale, but they do not produce one universal VRAM number. Precision, quantization, context length, architecture, runtime, and offload settings all change the result. Treat the following as planning guidance, not a promise that every model with the same parameter count will behave identically.
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7B-class models
A 7B model is often practical on a 12GB card when quantized, with enough remaining capacity for a normal context and runtime overhead. An 8GB card may run a compact or aggressively quantized 7B model, but you should expect tighter limits on context and fewer simultaneous applications. NVIDIA’s rough unquantized estimate for Mistral 7B Instruct v0.3 is approximately 14GB in NIM documentation version 1.7.0. That figure includes the scale of an unquantized deployment and is not a universal requirement for a quantized local file.
13B-class models
For a 13B model, 16GB is a more comfortable starting point for quantized inference than 8GB or 12GB. The exact quantization determines whether there is room for a useful context. If a 13B model leaves only a few hundred megabytes free after loading, lowering the context limit or choosing a smaller quantization may be necessary.
70B-class models
A 70B model generally exceeds the capacity of a single consumer GPU unless it is heavily quantized or split across devices. NVIDIA’s rough unquantized Llama 70B estimate is approximately 131GB in NIM documentation version 1.7.0. That is a rough NIM estimate, not a universal requirement, but it illustrates why 70B deployments commonly use multiple GPUs, CPU-plus-GPU offload, or substantial quantization.
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Do not interpret “70B runs locally” as “70B runs at full speed on one 12GB card.” It may load by placing part of the model in system RAM, yet generation speed and responsiveness can be much lower than an all-GPU configuration.
Use this six-step sizing method
- Choose the model scale and quality target. Start with the task: a 4B model may be adequate for lightweight chat, while a larger model can improve reasoning or instruction following. Define a practical target such as 4B, 8B, 12B, 27B, or 70B.
- Select a quantization. Lower-bit formats reduce weight memory. llama.cpp supports 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization. More aggressive quantization saves capacity but can reduce response quality, so do not select the smallest file automatically.
- Add context requirements. Decide how long prompts and conversations must be. Account for document retrieval, agent tool results, and multi-turn history, not just the model’s advertised maximum context. Longer context requires more memory for the KV cache.
- Reserve operating-system and runtime headroom. NVIDIA’s NIM guidance lists 5–10GB for the operating system and other processes. The amount you can actually reserve depends on your desktop, drivers, inference server, and other GPU applications.
- Verify architecture and backend support. Select a GPU and runtime that support the model format and your operating system. CUDA, HIP, Metal, Vulkan, and other backends have different compatibility and optimization profiles.
- Plan a fallback before buying. If the model does not fit, first lower context or choose a smaller or more aggressively quantized model. If quality requirements rule that out, use CPU-plus-GPU offload or multiple GPUs and accept the expected performance penalty.
Why quantization changes the GPU you need
Quantization stores weights with fewer bits per parameter. That reduces the memory occupied by weights and can make a model practical on a smaller card. The trade-off is that aggressive quantization can deteriorate response quality. The best choice is the lowest-bit format that still meets your accuracy and instruction-following needs, with enough room left for context and runtime allocations.
llama.cpp exposes a broad range of integer formats, from 1.5-bit through 8-bit. The exact memory use and quality effect depend on the model and quantization implementation, so compare the format named by the model publisher rather than assuming that two files with the same parameter count have the same requirements.
When to choose a less aggressive format
- You have adequate VRAM and care more about output fidelity than fitting the smallest possible model.
- Your workload involves code, structured output, tool calls, or long chains of reasoning where quality loss is noticeable.
- You need a larger context and the less aggressive format still leaves enough headroom.
When a lower-bit format is reasonable
- You are capacity-limited and a smaller quantization is the difference between loading and not loading.
- You are testing a model, running short conversations, or prioritizing local availability over maximum quality.
- You are using CPU-plus-GPU offload and want to reduce the amount of data moved through system memory.
Context length and KV cache are easy to underestimate
Every additional token in the prompt or conversation increases memory pressure. A short chat may work at a setting that fails when you paste a long report. Retrieval-augmented generation adds retrieved passages; agents add tool results and intermediate messages; multi-turn sessions retain history. All of these consume context capacity.
NVIDIA identifies context length and KV-cache configuration as major variables in actual deployment. If a model loads but fails when generation starts, the context allocation or runtime buffers may be the cause rather than the weights themselves.
How to leave useful context headroom
- Start with the context length you genuinely need instead of the maximum advertised by the model.
- Keep a safety margin after loading the model; do not treat the final few hundred megabytes as usable capacity.
- Test with your longest realistic prompt, including retrieved documents or tool output.
- If memory errors appear only during long conversations, lower the context limit or reduce KV-cache pressure before replacing the GPU.
Headroom and official rough memory examples
NVIDIA’s NIM documentation version 1.7.0 gives these rough unquantized estimates:
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| Model example | Approximate memory estimate | Qualification |
|---|---|---|
| Llama 8B | Approximately 15GB | Rough NVIDIA NIM estimate; unquantized example, documentation version 1.7.0. |
| Mistral 7B Instruct v0.3 | Approximately 14GB | Rough NVIDIA NIM estimate; unquantized example, documentation version 1.7.0. |
| Llama 70B | Approximately 131GB | Rough NVIDIA NIM estimate; unquantized example, documentation version 1.7.0. |
| Mixtral 8x7B Instruct v0.1 | Approximately 88GB | Rough NVIDIA NIM estimate; unquantized example, documentation version 1.7.0. |
These numbers are not universal requirements for every local runtime. Quantization, context, batch settings, implementation details, and offload can materially change the allocation. They are useful for understanding scale and for avoiding the mistake of equating a compressed download with an unqualified deployment requirement.
Can CPU offload run a model that does not fit in VRAM?
Yes. llama.cpp supports hybrid CPU-plus-GPU inference, allowing a model larger than total GPU VRAM to run by placing some layers or data in system RAM. This is a capacity escape hatch, not an equivalent substitute for full-GPU execution.
What you gain
- You can run a model that exceeds the VRAM of one card.
- You may avoid buying additional GPU memory for occasional or experimental use.
- You can combine a discrete GPU with available system RAM.
What you give up
- Data crossing between system memory and the GPU is slower than keeping the working set in VRAM.
- Generation speed and time to first token can fall, sometimes substantially, depending on the split and workload.
- System RAM capacity and memory bandwidth become additional constraints.
Use offload when model capacity matters more than maximum throughput. For interactive work, first try a smaller model, lower context, or a quantization that fits fully in VRAM. For a large model used regularly, multiple GPUs or a higher-capacity card may provide a better experience.
When should you use multiple GPUs?
Multiple GPUs can divide a model that does not fit on one card. The practical result depends on the backend, interconnect, driver support, and whether the cards have compatible capabilities. Two cards do not automatically behave like one card with their VRAM added at the same speed.
- Capacity: combined memory can make a larger model load.
- Communication: layers or tensors may need to move between GPUs, adding transfer overhead.
- Balance: a much smaller card can become a bottleneck if the split is uneven.
- Power and cooling: additional cards require physical space, adequate power delivery, and airflow.
- Software: confirm that your chosen backend supports the model format and multi-GPU arrangement.
For a two-card setup, homogeneous GPUs are generally easier to configure and balance than mismatched cards, but compatibility must still be checked for the specific backend and operating system.
GPU specifications that matter after VRAM
Once two GPUs can hold the same model with comparable context headroom, compare the factors that determine real local-inference behavior:
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| Factor | Why it matters | Question to ask |
|---|---|---|
| Usable VRAM headroom | Determines whether context and runtime allocations fit without failures. | How much remains after loading the model and normal applications? |
| Memory bandwidth | Inference often moves large weight tensors repeatedly, so bandwidth can affect generation speed. | Is the card faster for this model and backend, not merely on paper? |
| Backend support | CUDA, HIP, Metal, Vulkan, and other paths differ in features and optimization. | Does the runtime support this GPU architecture and model format? |
| Expected tokens per second | Throughput varies with architecture, quantization, context, drivers, and workload. | Are comparisons measured with the same model, format, context, and backend? |
| Power and noise | A faster card can require more cooling and create more noise. | Can your case, power supply, and room tolerate sustained load? |
| Physical fit | Large cards can block slots or fail to fit in a compact case. | Are slot width, length, connectors, and airflow adequate? |
| Multi-GPU practicality | Extra cards may help capacity but add transfer and configuration costs. | Can the backend split this model effectively across your cards? |
Do not compare only shader counts or tensor-core counts. Local inference is constrained by memory capacity and by the particular model/backend combination. There is no universal tokens-per-second figure that applies across all models and configurations.
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NVIDIA recommends selecting a backend based on operating system, model format, GPU architecture and memory, API requirements, and throughput target. Common local choices include llama.cpp, Ollama, LM Studio, and vLLM. Their supported formats, device backends, server features, and configuration controls differ.
llama.cpp
llama.cpp is designed for LLM and VLM inference with minimal setup and performance across a wide range of hardware, locally and in the cloud. It is a strong option when you need explicit control over quantized formats, context, CPU-plus-GPU hybrid execution, or GPU layers.
Ollama and LM Studio
These applications can simplify model management and local chat. Check which quantized format they use, how they expose context settings, and whether they provide the GPU-layer or offload controls you need. A simplified interface does not remove the underlying VRAM requirements.
vLLM
vLLM is oriented toward serving and throughput-focused deployments. Confirm support for your GPU, model format, quantization method, and desired API before treating it as a drop-in replacement for a desktop chat application.
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Out-of-memory error during model loading
- Close other GPU applications and check actual free VRAM.
- Lower the context limit and disable unnecessarily large batch or cache settings.
- Choose a smaller or more aggressively quantized model.
- Enable CPU-plus-GPU offload if your backend supports it.
- Move to a higher-capacity GPU or distribute the model across multiple GPUs.
The model loads, then fails on a long prompt
The weights fit, but the context or KV cache does not. Reduce context length, trim retained conversation history, limit retrieved documents, or adjust the backend’s KV-cache configuration.
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Generation is much slower than expected
Check whether layers were silently offloaded to system RAM, whether the selected backend is using the GPU, and whether your context has grown substantially. Compare only like-for-like configurations: same model, quantization, context, driver, backend, and workload.
Quality is worse after changing quantization
The lower-bit format may be too aggressive for your task. Move to a higher-bit quantization if VRAM permits, or choose a smaller model at a less aggressive quantization instead of forcing a large model into an extremely small footprint.
Recommended starting points by goal
Lowest-cost experimentation
Use a 6–8GB GPU with a 4B-class quantized model and a moderate context. This is suitable for learning local inference and short interactive sessions, provided you leave room for the runtime.
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A 12–16GB GPU is a practical target for approximately 9B–12B models. It offers more room for context and less aggressive quantization than an 8GB card, while remaining simpler than a multi-GPU setup.
Larger single-GPU models
With 24GB or more, target a 27B-class model when its selected quantization and context fit comfortably. Verify the exact model package and backend rather than relying only on the parameter count.
Very large models
For 70B-class workloads, plan around multiple GPUs, CPU-plus-GPU offload, or substantial quantization. Select the arrangement based on whether your priority is fitting the model, maintaining interactive speed, or preserving output quality.
A final buying checklist
- Identify the model size you actually need: 4B, 8B, 12B, 27B, 70B, or another target.
- Choose a supported quantization and account for its quality trade-off.
- Estimate your longest real context, including documents, tools, and conversation history.
- Reserve operating-system and runtime headroom; NVIDIA’s guidance uses 5–10GB as a rough allowance.
- Confirm that your operating system, GPU architecture, driver, backend, and model format work together.
- Prefer a card where the model fits fully in VRAM if interactive speed matters.
- Use CPU offload or multiple GPUs when capacity is the priority and you accept slower execution or extra complexity.
- Compare bandwidth, backend support, power, noise, physical fit, and multi-GPU practicality after capacity is satisfied.
The safest choice is not the card on which a model barely loads. It is the card that leaves enough VRAM for the context and runtime you will use, with a backend that supports your model and hardware. As NVIDIA’s guidance puts it, choose the most powerful model that fits comfortably in GPU memory.
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