Choose a GPU for local LLM inference by sizing the model’s total working set, not by matching the downloaded file to the number printed on a graphics card. The working set includes quantized weights, context and KV-cache memory, the inference runtime, and operating-system headroom. As a practical starting point, use a 6–8GB card for a 4B-class quantized model, 12–16GB for roughly 9B–12B models, and 24GB or more for a 27B-class model when its quantization and context fit. A 7B model can be workable on 8GB only with careful settings; a 13B model is more comfortable at 16GB or above; a 70B model normally requires several GPUs, CPU offload, or very aggressive quantization.
Quick answer: choose by usable VRAM tier
NVIDIA’s RTX local-LLM guidance provides useful starting tiers. They are not guarantees for every model, context length, backend, or operating system, but they are a better first filter than shader-core counts or the size of a model download.
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| Usable GPU VRAM | Reasonable starting target | What to expect |
|---|---|---|
| 6–8GB | Qwen 3.5 4B, quantized | Shorter context and modest runtime headroom; larger models generally need lower-bit quantization or offload. |
| 12–16GB | Qwen 3.5 9B or Gemma 4 12B | A practical range for many 7B–12B-class models, with room for ordinary context lengths if the selected quantization fits. |
| 24GB or more | Qwen 3.6 27B | Suitable for 27B-class models when weight, KV-cache, and runtime allocations stay within the card’s usable capacity. |
“Usable” matters because a card advertised with a given VRAM capacity still needs space for the driver, desktop, inference process, and other applications. Leave headroom rather than planning to occupy every last megabyte.
What the memory budget actually contains
NVIDIA’s PAIR documentation states: “A model’s download size is not its total memory requirement.” A file on disk primarily represents stored weights. During inference, those weights are loaded into memory and joined by temporary and persistent allocations.
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Model weights
Weights are the largest predictable component. Their footprint depends on parameter count, numerical format, and whether the model is dense or has a mixture-of-experts design. A nominal “7B” or “13B” label describes parameter scale, not a fixed number of gigabytes in VRAM.
Quantization level
llama.cpp supports integer quantization at 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit levels. Lower-bit formats reduce weight memory, allowing a larger model to fit, but the reduction is not free: NVIDIA warns that aggressive quantization can deteriorate response quality. Quantization also does not eliminate memory for context, KV cache, or runtime buffers.
| Quantization choice | Memory effect | Quality and compatibility consideration |
|---|---|---|
| 1.5–2-bit | Smallest weight footprint | Most aggressive quality trade-off; use only when capacity is the overriding constraint and the model/backend support it. |
| 3–4-bit | Substantially less memory than higher precision | Common capacity-oriented choices; verify that the particular quantized file and backend are supported. |
| 5–6-bit | More weight memory than 3–4-bit | Retains more numerical detail while still saving memory compared with 8-bit. |
| 8-bit | Largest integer-quantized footprint in this list | Usually gives the most headroom for quality among these integer formats, but may exceed a small card’s capacity once context is added. |
Do not compare two files solely by their extension or advertised size. Confirm the quantization scheme, model architecture, context setting, and backend before assuming that a file will load.
Context and KV-cache memory
Every token in the prompt and generated conversation consumes working memory. Longer context windows increase the KV cache, and NVIDIA identifies context length and KV-cache configuration as major variables in real deployments. Retrieval documents, agent tool output, system prompts, and accumulated multi-turn history can therefore turn a model that loads successfully into one that runs out of VRAM during use.
A short test prompt is not proof that a model is safe at the context length you intend to use. Size for the largest prompt and response history you will routinely send, not the smallest demonstration that starts the model.
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Runtime, driver, and operating-system headroom
The inference engine allocates buffers in addition to weights and KV cache. Your desktop, display server, browser, monitoring tools, and GPU driver may also reserve memory. Keep a deliberate margin instead of treating the card’s labeled capacity as available application memory.
Reference memory figures—and why they are only estimates
NVIDIA’s NIM documentation, version 1.7.0, lists rough unquantized memory examples and separately advises allowing about 5–10GB for the operating system and other processes. These figures illustrate scale; they are not universal requirements for every implementation.
| Model example | Approximate unquantized memory in NIM documentation 1.7.0 | How to use the figure |
|---|---|---|
| Llama 8B | About 15GB | A reminder that an 8B model at higher precision can exceed a 12GB card before other allocations. |
| Mistral 7B Instruct v0.3 | About 14GB | Shows why an 8GB card needs quantization and conservative context for a 7B-class model. |
| Mixtral 8x7B Instruct v0.1 | About 88GB | Mixture-of-experts naming does not imply a small memory requirement; plan for the documented footprint and runtime behavior. |
| Llama 70B | About 131GB | Far beyond one mainstream consumer card at the documented precision; multi-device or offload strategies are expected. |
The practical calculation is:
total working memory ≈ quantized weights + KV cache for your context + runtime buffers + operating-system headroom
Because each term changes with the model file, backend, context, and driver, there is no single VRAM number that universally defines “a 7B model” or “a 70B model.”
How much VRAM do 7B, 13B, and 70B models need?
7B-class models
A 7B model is the most approachable of the three. NVIDIA’s rough unquantized estimate for Mistral 7B Instruct v0.3 is about 14GB before adding the recommended 5–10GB of system and process headroom. On an 8GB GPU, use a supported low-bit quantization, keep context conservative, and expect less room for other applications. A 12–16GB card gives a more forgiving starting point for 7B-class models and allows more context or a less aggressive quantization, subject to the particular file.
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Do not infer performance from capacity alone. Two cards that both load the same 7B model can differ significantly in tokens per second because of memory bandwidth, architecture, drivers, and backend implementation.
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Thirteen-billion-parameter models sit above the 9B–12B tier in NVIDIA’s RTX guidance. A 16GB card can be viable with quantization and a controlled context, but the margin may disappear when you increase context, use a heavier quantization, or run a desktop and other GPU workloads at the same time. A 24GB card provides substantially more room for a 13B model, longer conversations, or a quality-preserving quantization, although the model’s actual allocations still decide whether it fits.
If your priority is reliable interactive use rather than the largest parameter count, a well-supported 9B–12B model with comfortable headroom can be a better experience than forcing a 13B model into a nearly full card.
70B-class models
NVIDIA’s NIM 1.7.0 example puts unquantized Llama 70B at roughly 131GB, illustrating why a single 24GB card cannot run that configuration entirely in VRAM. Quantized 70B files reduce weight memory, but context and runtime allocations remain, and the result may still exceed one GPU. Plan for multiple GPUs, CPU+GPU hybrid offload, or a smaller model. Those approaches trade speed, complexity, or quality for capacity; they are not equivalent to full-GPU execution.
Compare GPUs on more than the VRAM label
Once two cards can hold the same chosen model and context, evaluate the characteristics that determine usability:
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| Comparison axis | Why it matters for local inference | Questions to ask |
|---|---|---|
| Usable VRAM headroom | Prevents out-of-memory errors as context and runtime buffers grow. | How much remains after the driver, display, and your normal applications start? |
| Memory bandwidth | Influences how quickly weights and cache data can be moved during generation. | Is the card’s bandwidth appropriate for the model size and target responsiveness? |
| Backend support | Features and kernels vary across CUDA, HIP, Metal, Vulkan, and other backends. | Does the chosen engine support this architecture and quantized format? |
| Expected throughput | Tokens per second depend on model architecture, quantization, context, drivers, and workload. | Are there measurements for your exact model, backend, and context rather than a generic GPU claim? |
| Power and noise | Long inference sessions can make cooling, electricity, and acoustics important. | Can your power supply and cooling sustain the card without unacceptable noise? |
| Physical fit | Large cards may not fit the case or leave room for airflow and additional GPUs. | Check slot width, length, connectors, and motherboard spacing. |
| Multi-GPU practicality | Several cards can increase capacity but add topology, power, and software complexity. | Can your platform host the cards, and does the backend distribute the model as needed? |
Do not rank cards by shader or tensor-core counts alone. Local inference is constrained by the specific model, memory capacity, bandwidth, and software path.
Choose an inference backend and hardware path together
NVIDIA recommends selecting a backend using the operating system, model format, GPU architecture and memory, API requirements, and throughput target. Named local options include llama.cpp, Ollama, LM Studio, and vLLM. They expose different packaging, APIs, and hardware integrations, so select the engine before finalizing a model file or GPU.
| Backend option | Use it as a selection question | What to verify |
|---|---|---|
| llama.cpp | Do you need a broadly portable local engine with CPU+GPU hybrid inference? | Supported model format, quantization variant, GPU backend, context configuration, and offload controls. |
| Ollama | Do you want a packaged local workflow and API around supported models? | Whether the model build, quantization, operating system, and GPU are supported with the context you require. |
| LM Studio | Do you prefer a desktop-oriented local model workflow? | The application’s supported formats, GPU acceleration path, memory reporting, and maximum context behavior. |
| vLLM | Do you need a serving-oriented API or throughput-focused deployment? | GPU architecture, supported model family, memory utilization settings, and multi-GPU requirements. |
CUDA, HIP, Metal, Vulkan, and other backends have different compatibility and optimization profiles. A model that works on one backend may require a different file, driver, or setting on another.
A step-by-step sizing method
- Define the model target. Write down the parameter class and quality requirement: for example, 4B, 8B, 12B, 27B, or 70B. Decide whether answer quality or fitting a particular card is the primary constraint.
- Select a quantization family. Start with the highest-bit format that leaves room for context and runtime allocations. Move to lower-bit quantization only when the quality and capacity trade-off is acceptable.
- Set the real context requirement. Include system instructions, retrieved documents, tool results, conversation history, and the desired generated answer. A benchmark using a short prompt is not representative if your application uses long documents.
- Add headroom. Reserve space for the operating system, display, driver, inference runtime, and other GPU processes. NVIDIA’s NIM guidance uses roughly 5–10GB for the OS and other processes in its examples.
- Check the backend and architecture. Confirm that the operating system, GPU backend, model format, quantization, and API requirements are all supported together.
- Plan the fallback before buying. Decide whether you will reduce context, choose a smaller or more aggressively quantized model, offload layers to system RAM, or add a second GPU if the first configuration does not fit.
What to do when the model does not fit
First reduce context
Lowering the context limit reduces KV-cache demand without changing the model’s weights. This is the least disruptive fix when your workload can tolerate shorter conversations or smaller retrieval batches. Keep enough context for the task; an artificially tiny window can harm answer quality even though the model starts.
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A smaller parameter class often preserves a better interactive experience than pushing a large model through severe memory pressure. If the model family is important, compare quantization levels and test representative prompts for quality regressions before adopting the most aggressive option.
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Use CPU+GPU hybrid inference
llama.cpp supports CPU+GPU hybrid inference for models larger than total GPU VRAM. Offloaded layers or tensors use system RAM while the GPU handles the remainder. This is a capacity escape hatch, not a free VRAM extension: moving data across the CPU–GPU boundary generally reduces throughput and may increase latency. The amount and placement of offload, system-memory speed, interconnect, and context all affect the result.
Use multiple GPUs
Multiple GPUs can divide a model when one card cannot hold it. Homogeneous cards simplify planning, but the total advertised VRAM is not automatically a single pool with single-card performance. You must account for the backend’s partitioning method, PCIe or other interconnect limits, power, cooling, and the memory required on each device. A multi-GPU setup is worthwhile when capacity is the priority and the expected speed penalty is acceptable.
Common sizing mistakes and fixes
- Mistake: matching VRAM to the file size. Fix: add KV cache, runtime buffers, and operating-system headroom to the weight footprint.
- Mistake: assuming a 7B, 13B, or 70B label implies one fixed requirement. Fix: record the exact model variant, quantization, context length, and backend.
- Mistake: planning to use all labeled VRAM. Fix: leave room for the driver, desktop, and other processes; an allocation failure can occur before the card appears full in a basic monitor.
- Mistake: testing only a short prompt. Fix: test the largest normal context, including retrieval text, tool output, and conversation history.
- Mistake: treating CPU offload as equal to full-GPU execution. Fix: use offload when capacity matters more than latency, and measure the complete workload.
- Mistake: comparing only compute-core specifications. Fix: compare memory capacity, bandwidth, backend support, power, physical fit, and multi-GPU practicality.
- Mistake: selecting a GPU before selecting the software path. Fix: verify the model format and backend support for the operating system and architecture you intend to use.
- Mistake: assuming a mixture-of-experts name means low memory. Fix: use the documented footprint for the complete model; NVIDIA’s Mixtral 8x7B Instruct v0.1 example is about 88GB unquantized.
Practical decision examples
You already own an 8GB GPU
Start with a 4B-class quantized model and a conservative context. A 7B model may work if you select a low-bit file, close competing GPU applications, and accept less context or slower execution. If you repeatedly hit out-of-memory errors, reduce context first, then move to a smaller or more aggressively quantized model before considering offload.
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You are choosing between 12GB and 16GB
Both sit in the range NVIDIA associates with 9B–12B models, but the extra headroom of a 16GB card can determine whether your intended context, desktop, and runtime fit simultaneously. Compare bandwidth and backend support after confirming that both cards can hold the same quantized model with your target context.
You want a 27B model
Use 24GB or more as the starting tier identified by NVIDIA’s RTX guidance. Verify the exact Qwen 3.6 27B quantization and context allocation; a nominally suitable card can still fail if the runtime reserves more memory than expected. If it does not fit, reduce context or use CPU+GPU or multi-GPU execution.
You want a 70B model for occasional use
Do not design around a single 24GB card at unquantized precision: NVIDIA’s NIM 1.7.0 example is approximately 131GB. Estimate the quantized working set, then decide whether several GPUs, hybrid offload, or a smaller model best matches your tolerance for cost, complexity, and latency.
Final buying checklist
- Exact model family and parameter class selected.
- Quantization level and file format confirmed for the chosen backend.
- Context limit sized for real prompts, retrieval, tools, and history.
- VRAM budget includes runtime allocations and 5–10GB of operating-system/process headroom where applicable.
- GPU architecture and backend support checked for the operating system.
- Memory bandwidth, power, cooling, case clearance, and connectors verified.
- Fallback chosen: lower context, smaller model, lower-bit quantization, CPU offload, or multiple GPUs.
- Performance expectations based on the exact model and workload, not a universal tokens-per-second claim.
The durable rule is simple: buy enough usable VRAM for weights, context, and overhead, then choose the fastest and best-supported architecture that leaves margin. NVIDIA’s guidance puts it well: “In general, use the most powerful model that fits comfortably in your GPU’s memory.” Comfortably—not merely barely loaded—is what keeps local inference stable when prompts get longer and the runtime does real work.
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