There is no single VRAM minimum for running a local large language model (LLM). Start with the model’s parameter count and weight format, then budget additional memory for context-dependent KV cache, runtime allocations and other workload-specific needs. A model whose weights fit may still fail at your intended context length.
If you are asking, “How much VRAM do you need to run local LLMs with Ollama?”, use the same sizing approach—but check the model format and the backend’s allocation behavior rather than relying on a universal Ollama-specific threshold.
As an Amazon Associate I earn from qualifying purchases.
Estimate weight memory first
A useful first estimate is:
weight memory per GPU = total parameters × bytes per parameter ÷ tensor parallelism
Tensor parallelism means splitting a model’s weights across multiple GPUs. NVIDIA’s documented bytes-per-parameter examples are BF16: 2, FP16: 2, FP8: 1, and INT4/NVFP4: 0.5. This arithmetic estimates weight memory only; it is not the total VRAM requirement. NVIDIA’s GPU-memory troubleshooting documentation gives the method and examples.
#1 Best Overall
- Chipset: AMD RX 7900 XT
- Memory: 20GB GDDR6
- AMD Triple Fan Cooling Solution
- Boost Clock: Up to 2400 MHz
What the estimate looks like for documented examples
| Example | Documented memory or size | What the figure means |
|---|---|---|
| Llama 3.1 8B, BF16, one GPU | 16 GB | NVIDIA’s estimated weight memory; it says a single 24 GB GPU can hold these weights with room for KV cache and overhead. This is an example, not a guarantee for every 8B model, runtime or context. |
| Llama 3.3 70B, BF16, four GPUs | 35 GB per GPU | NVIDIA’s example estimate for weights; remaining space for KV cache varies. |
| Llama 3.1 8B, original file | 32.1 GB | Size listed in the llama.cpp quantization documentation; not a live inference allocation. |
| Llama 3.1 8B, Q4_K_M | 4.9 GB | Size listed in the same documentation; not a complete VRAM requirement. |
The NVIDIA documentation is rolling and displays no publication date; these NVIDIA figures were accessed in 2026, which is not a claimed publication year. The file-size examples are from the ggml-org/llama.cpp quantization README, tag studio-2026.1.1, accessed in 2026.
Budget for more than the model weights
Inference uses memory beyond stored weights. NVIDIA identifies KV cache, peak activations, communication buffers, CUDA context and other unaccounted allocations as part of the GPU-memory picture. Adapters and model-specific state can add further demand. The amount depends on the model, runtime and workload.
Rank #2
- System Compatibility Note: This 2-slot card measures 271 x 112 x 39 mm and requires a single 12V-2x6-pin power connector. Please verify chassis and PSU compatibility before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- Professional Intel Arc Pro B70 GPU: Built on the Intel Xe2-HPG architecture, it features 32 Xe cores and 256 XMX engines, designed to accelerate AI, rendering, and complex visualization workloads.
- Massive 32GB GDDR6 VRAM: Equipped with 32GB of high-speed GDDR6 memory on a 256-bit bus, running at 19 Gbps, which allows for handling large AI models and complex datasets locally.
- High-Performance Engine Clock: Delivers an engine clock of 2540 MHz, providing the compute power needed for demanding professional applications and AI inference.
Context length and KV cache
The KV cache stores information used while processing the prompt and generating tokens. A longer context can require more cache capacity. NVIDIA notes that a common failure is being unable to allocate cache for a model’s long native context after weights and overhead have already consumed much of the available memory. So a successful load at a short context does not establish that the model will run at a longer one.
Recommended Free Tools
Other workload demands
Peak activations and runtime buffers also contribute to memory use. Concurrent requests, multimodal inputs and backend allocation behavior can change the practical budget. Leave capacity for display use and other GPU processes; the memory visible to a model may be less than the card’s advertised capacity.
Rank #3
- Powered by the NVIDIA Blackwell architecture and DLSS 4. System Requirements: Minimum 850W PSU with 16-pin 12V-2x6 (12VHPWR) connector required. Verify before purchasing.
- Military-grade components deliver rock-solid power and longer lifespan for ultimate durability. Compatibility: 348mm (13.7") length, 3.6 slots, 4.3 lbs. Confirm case clearance and slot spacing. GPU bracket included.
- Protective PCB coating helps protect against short circuits caused by moisture, dust, or debris
- 3.6-slot design with massive fin array optimized for airflow from three Axial-tech fans
- Phase-change GPU thermal pad helps ensure optimal thermal performance and longevity, outlasting traditional thermal paste for graphics cards under heavy loads
Quantization can shrink weights, but does not guarantee a fit
Quantization stores weights using fewer bits, often reducing model-file size. In the llama.cpp example above, the documented Llama 3.1 8B Q4_K_M file is 4.9 GB versus 32.1 GB for the original. That is a substantial reduction in stored size, but it does not mean inference needs only 4.9 GB of VRAM: cache, activations and runtime allocations remain. llama.cpp also notes that quantization methods differ in disk size and inference speed. Evaluate quality and speed for your task rather than treating a smaller file as an automatic upgrade.
Choose the model and backend for the workload
Do not select a GPU from a bare parameter count or a model-file size alone. First decide what you need the model to do, how much context and concurrency you expect, and which inference backend and model format you plan to use. Hardware support and memory partitioning can differ between backends.
Rank #4
- System Compatibility Note: 2.5-slot card, 290x123x51mm, two 8-pin power, recommended 700W PSU. Verify chassis clearance before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- AMD RDNA 4 Architecture: RX 9070 GPU with 56 CUs, 3584 stream processors, 3rd gen RT and 2nd gen AI accelerators – built for 1440p/4K gaming.
- Factory Overclocked Performance: Boost clock up to 2520 MHz, game clock 2070 MHz – delivers smooth, high-framerate gaming out of the box.
- 16GB GDDR6 on 256-Bit Bus: High-speed 20 Gbps memory provides exceptional bandwidth for 4K textures, ray tracing, and demanding workloads.
- Task quality: compare candidate models on the work you actually need done, not just parameter count.
- Representation: check the exact precision or quantized model file and consider the quality and speed tradeoffs.
- Memory headroom: compare usable VRAM with weights plus cache, runtime allocations and other state.
- Context and concurrency: size for the prompt length and number of simultaneous requests you intend to run.
- Compatibility and performance: check operating-system, GPU-architecture and model-format support, along with throughput needs.
- Fallbacks: decide whether CPU/GPU hybrid inference is acceptable if the model exceeds VRAM.
NVIDIA’s local AI guidance recommends defining VRAM and performance requirements, shortlisting models against benchmarks, and evaluating candidates on a task-specific dataset. It lists Q4_K_M as an option for llama.cpp and NVFP4 for vLLM or PyTorch; those are shortlist suggestions, not a substitute for testing your use case.
A practical sizing workflow
- Choose the model and runtime. Decide which model and inference backend you intend to use before judging whether a GPU is sufficient.
- Find the actual model format and file size. Check the model documentation for parameter count, weight precision or quantization, and the specific downloadable file. File size helps compare representations but is not the full runtime budget.
- Estimate the weights. Use parameter count times bytes per parameter; for a multi-GPU setup, account for how your backend partitions the model.
- Add runtime and context memory. Allow for KV cache, activations, buffers, CUDA/runtime overhead, adapters and model-specific state. Check startup logs or backend memory estimates when available.
- Compare against usable VRAM. Leave headroom for the display, other processes and allocations not captured by a simple estimate.
- Test the real workload. Try the intended prompt length, generated output, concurrency and any multimodal inputs, then check both memory use and throughput.
What to change when the model does not fit
- Lower the context length. This can reduce cache demand. NVIDIA’s DGX Spark playbook gives lowering context to 4096 as an example remedy for a CUDA out-of-memory error; that setting is specific to its example, not a universal recommended context.
- Use a smaller quantization or model. A more compact representation can reduce weight memory, while a smaller model may reduce overall demand. Check task quality and speed after changing formats.
- Try hybrid CPU/GPU inference. llama.cpp documents partially accelerating models larger than total VRAM by using both CPU and GPU. This can make a larger model usable, but the documentation does not promise a particular speed.
- Reduce competing allocations. Close GPU-heavy applications or reduce concurrent work, then retry and inspect the backend’s logs for the allocation that failed.
The NVIDIA DGX Spark llama.cpp playbook also describes an example with about 30 GB of free memory for the model, separately requiring sufficient unified memory for KV cache. Its figures and remedies apply to that platform and configuration, not to GPUs generally.
Quick Recap
Best Value
- AI Performance: 767 AI TOPS
- OC mode: 2632 MHz (OC mode)/ 2602 MHz (Default mode)
- Powered by the NVIDIA Blackwell architecture and DLSS 4
- Axial-tech fan design features a smaller fan hub that facilitates longer blades and a barrier ring that increases downward air pressure
- A 2.5-slot design maximizes compatibility and cooling efficiency for superior performance in small chassis
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
