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There is no single RAM or VRAM number that guarantees every local AI model will run. The practical requirement is the memory available to your inference backend after accounting for the model’s parameter count, quantization, context length, KV cache, runtime overhead, and other applications. As current starting points, NVIDIA places Qwen 3.5 4B on GPUs with 6–8 GB of VRAM, Qwen 3.5 9B or Gemma 4 12B on 12–16 GB, and Qwen 3.6 27B on 24 GB or more. Those are vendor examples, not universal minimums. You must check the exact model and format you intend to use.
What is the short answer for RAM and VRAM?
For GPU inference, use these as rough starting tiers from NVIDIA’s current local-LLM guidance:
| Available GPU memory | Example starting point | How to interpret it |
|---|---|---|
| 6–8 GB VRAM | Qwen 3.5 4B | Small-model tier; context and runtime headroom still matter |
| 12–16 GB VRAM | Qwen 3.5 9B or Gemma 4 12B | Midrange tier for a quantized model and moderate context |
| 24 GB or more VRAM | Qwen 3.6 27B | Larger-model example; verify the selected quantization and context |
| 64 GB or 128 GB unified memory | DGX Spark configurations | NVIDIA platform examples, not a general consumer minimum |
System RAM is the main pool for CPU-only inference. A discrete-GPU setup normally needs enough VRAM for the layers you want on the GPU, plus enough host RAM for the operating system, runtime, model data and any layers you offload. Apple Silicon and other unified-memory systems use one pool shared by CPU and GPU, so their advertised memory must cover both compute and system activity. There is no authoritative RAM-to-VRAM multiplier that works across every architecture, operating system and backend.
NVIDIA’s llama.cpp playbook summarizes the practical rule as: “Memory determines what you can run.” The same model can fit in one application and fail in another because the applications use different quantization formats, cache settings or offload strategies.
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Why can’t a model’s parameter count give one exact memory requirement?
Parameter count describes the scale of the weights, but it does not describe the complete working set in memory. Four models with similar parameter counts can have different architecture, precision, tokenizer, runtime and cache behavior.
Weights are only the starting point
Model weights occupy the largest predictable block. Their size depends on precision: native floating-point weights use more memory than 8-bit or 4-bit representations. Sparse and mixture-of-experts models add another complication: the total stored weights and the parameters active for each token are not the same thing. A model’s own documentation should identify which number matters for the downloadable file and for inference.
Context creates a separate cache
During generation, the backend stores attention keys and values (the KV cache) for the prompt and generated tokens. A longer context window, more simultaneous conversations, or larger batches can increase that cache substantially. NVIDIA’s llama.cpp example asks for about 30 GB of free memory for a particular Qwen3.6-35B-A3B MTP configuration because the estimate includes both model and cache needs; it is not a universal requirement for every 35B model.
The runtime and operating system consume headroom
CUDA or another accelerator runtime, temporary buffers, the display driver, the desktop, browser tabs and background applications all reduce memory available to the model. A card advertised with a particular VRAM capacity does not give that entire capacity to the inference process. Leave margin rather than sizing to the last megabyte.
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How much memory do common model sizes need without quantization?
Yale Center for Research Computing publishes approximate VRAM figures for unquantized inference. They are useful for showing scale, but they should not be mixed with the smaller, quantized vendor examples above.
| Model size | Approximate unquantized VRAM | Qualification |
|---|---|---|
| 7B | 10+ GB | Approximate inference estimate; check the model’s page |
| 13B | 20+ GB | Approximate, unquantized figure |
| 30B | 40+ GB | Approximate, unquantized figure |
| 70B | 80+ GB | Approximate, unquantized figure |
| 305B | 400+ GB | Approximate, unquantized figure |
These values are not purchase recommendations. They omit the differences introduced by quantization, context length, batching and offload. Yale advises checking each model’s current Hugging Face or Ollama documentation for exact requirements.
How should you calculate the requirement for a specific model?
Use the following workflow before buying hardware or downloading a large model.
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- Identify the exact model variant. Record the parameter count, architecture (dense or mixture-of-experts), supported context window and the file format. “An 8B model” is not precise enough if several revisions or formats exist.
- Choose the quantization you will actually run. Note whether the file is native precision, 8-bit, 4-bit or another format, and whether your backend supports it. A quantized file can use far less memory but may trade some output quality or speed.
- Set a realistic context and concurrency target. A 4,000-token chat, a 32,000-token coding session and four simultaneous requests have different KV-cache needs even when they use the same weights.
- Check the backend’s memory model. Find out whether it keeps all layers in VRAM, automatically offloads layers to system RAM, or uses unified memory. Offloading may make a model start while reducing generation speed.
- Measure usable memory, not the headline capacity. Close unnecessary applications, inspect free VRAM or unified memory, and account for memory reserved by the display and driver. On a CPU-only machine, check available RAM after the operating system has loaded.
- Add operating margin. Reserve space for the KV cache, temporary work buffers and normal application activity. A model that only fits when the machine is idle is not comfortably sized for daily use.
- Validate with the model publisher’s instructions. Treat a model card or runtime-specific estimate as authoritative for that combination, then confirm that your operating system and accelerator are supported.
What is the difference between RAM, VRAM and unified memory?
Discrete GPU VRAM
VRAM is the GPU’s local, high-bandwidth memory. Keeping weights and cache there usually gives the most predictable accelerator performance, but the capacity is fixed. If the model exceeds it, a backend may place some data in host RAM or refuse to load the model.
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System RAM for CPU inference
With CPU-only inference, system RAM holds the model and its working data. The same factors still apply: precision, context, cache and runtime overhead. More RAM can allow a larger model, but memory bandwidth and CPU capability determine how responsive generation feels; capacity alone does not predict tokens per second.
Unified memory
Unified-memory computers share one physical pool between CPU, GPU and the operating system. NVIDIA lists 64 GB and 128 GB unified-memory DGX Spark configurations for its documented llama.cpp platform example. Those figures demonstrate a platform design, not a claim that any consumer computer with the same capacity behaves identically. Compare the memory available to the model after system use, not just the number printed on the specification sheet.
How much does quantization reduce memory?
Quantization stores weights with fewer bits. It generally lowers memory use and can make local inference possible on a smaller GPU, with a quality and sometimes performance trade-off.
Puget Systems measured Llama 3.1 8B in a tested configuration. Its native BF16 Transformers setup used just over 15 GB of VRAM; the article describes 8-bit and 4-bit llama.cpp versions as requiring about one-half and one-quarter of the native-precision VRAM, respectively. Those ratios describe that model, software and test setup. They are not a universal formula for every model, backend or context length. Cache and runtime memory do not necessarily shrink in the same proportion as the weights.
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What happens when the model does not fit entirely in VRAM?
Many backends support partial GPU offload. Some layers remain in VRAM while others, and sometimes parts of the cache, reside in system RAM. This can prevent an out-of-memory error, but transfers across the system bus add latency and can reduce throughput. The exact behavior depends on the backend, driver and hardware.
- Reduce the context window if the model loads but fails when a long prompt is submitted; this lowers KV-cache demand.
- Use a smaller quantization supported by your backend, accepting the model-quality trade-off.
- Lower batch size or concurrent requests when generation works for one conversation but fails under load.
- Enable controlled layer offload if your runtime supports it, then test speed and stability rather than assuming it is equivalent to full-VRAM execution.
- Close GPU applications such as games, video editors or extra model processes that reserve VRAM.
- Choose a smaller model when the workload is interactive and offloading makes response times unacceptable.
Is a 24 GB graphics card enough for a large local model?
Twenty-four gigabytes is a useful tier, not a guarantee. NVIDIA uses 24 GB or more as a starting point for its Qwen 3.6 27B example, while its documentation also emphasizes leaving room for the cache. A 24 GB card may run a particular 27B quantization at a moderate context, yet fail with a larger format, a very long context or several concurrent requests.
When comparing hardware, evaluate usable capacity, memory bandwidth, backend support, power and form factor, and whether your workload needs long context or concurrency. Current evidence here establishes memory-capacity tiers, not a universal price, speed or cross-vendor compatibility table. Verify the exact accelerator and software combination before purchase.
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How do context length and concurrent users change the answer?
Context length controls how many prior tokens the attention mechanism must retain. As the prompt and conversation grow, the KV cache grows as well. Concurrent sessions multiply that cache demand, and batching can add temporary buffers. Therefore, a model that fits for short, single-user chats may need substantially more memory for document analysis, coding repositories or a shared service.
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Set the maximum context and number of simultaneous requests in your runtime configuration, then observe memory at that target. Do not size from the model-file download alone.
What should you check before buying memory?
- The model revision and quantization file you intend to use.
- The inference backend’s supported GPU, CPU and unified-memory paths.
- The maximum context window and expected number of simultaneous requests.
- Free VRAM or unified memory after the operating system and display are running.
- Whether your workload can tolerate CPU or system-RAM offload.
- Memory bandwidth and driver support, which affect responsiveness even when capacity is sufficient.
- A margin for cache growth, temporary buffers and ordinary desktop applications.
Because the target model, quantization, context, backend, operating system and budget differ from reader to reader, no responsible recommendation can reduce the decision to one RAM or VRAM number. Start with the model publisher’s current requirements, apply the workload conditions above, and test the exact configuration.
Frequently Asked Questions
Can system RAM replace insufficient GPU VRAM?
Some inference backends can offload layers or cache data to host RAM, so a model may load even when it exceeds local VRAM. The trade-off is usually lower performance, and support varies by backend; treat offload as a compatibility option rather than equivalent capacity.
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Why does a model fit when idle but fail during a long conversation?
The weights may be unchanged while the KV cache expands with the conversation’s context. Lowering the maximum context or ending old sessions can release that cache, provided the backend exposes those controls.
Does a 4-bit file always use exactly one-quarter of the memory?
No. The one-quarter figure comes from a Puget Systems test of Llama 3.1 8B relative to its native setup. Metadata, cache, temporary buffers and runtime implementation mean other models and applications can show different savings.
What does “30 GB free” mean in NVIDIA’s llama.cpp example?
It is an estimate for a specific Qwen3.6-35B-A3B MTP configuration and includes model and KV-cache needs. It should not be interpreted as the minimum RAM or VRAM for every 35B model.
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