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LLM GPU VRAM Calculator

Calculate exact GPU memory requirements, KV cache allocation, and quantization feasibility for running open-source LLMs locally or in the cloud.

Select Open Source Model:

671B ParametersMLA AttentionSparse MoE (37B active)

378.1 GBMinimum Required VRAM

434.8 GBRecommended (15% headroom)

375.8 GBWeights (Q4_K_M)

1.0 GBKV Cache (8k tokens)

VRAM Allocation Composition

Weights: 99% • KV Cache: 0% • Buffer: 0%

Weights (375.8 GB)KV Cache (1.0 GB)CUDA Buffer (1.3 GB)

Total: 378.1 GB

Quantization Precision

95.0% Quality Retention

Quantization reduces weight precision from 16-bit to lower bit-depths with minimal perceptual degradation.

Recommendation: Industry standard for consumer GPU hosting and Ollama.

Context Length & KV Cache

8,192 tokens

Longer context windows expand the KV cache proportionally to model depth and attention heads.

KV Cache Precision

Concurrent Batch: 1

Hardware Compatibility Matrix (Will It Run?)

Verified against consumer GeForce cards, Apple Silicon unified memory, and datacenter accelerators.

Requires 16x RTX 4090 (24GB) or 5x H100 (80GB)

Consumer GPUOOM

RTX 3060 (12GB)

Budget desktop GPU for 7B-8B quantized models.

Total Capacity:12 GB

Headroom:-366.1 GB

Consumer GPUOOM

RTX 4070 (12GB)

Fast Ada Lovelace card for 7B-14B models.

Total Capacity:12 GB

Headroom:-366.1 GB

Consumer GPUOOM

RTX 4080 (16GB)

High-bandwidth 16GB GPU for 14B Q5/Q8 and 32B Q4.

Total Capacity:16 GB

Headroom:-362.1 GB

Consumer GPUOOM

RTX 3090 / 4090 (24GB)

Gold standard consumer card. Runs 32B comfortably or 70B Q2/Q3.

Total Capacity:24 GB

Headroom:-354.1 GB

Workstation / DualOOM

Dual RTX 3090/4090 (48GB)

Dual GPU rig capable of running 70B Q4_K_M with 32k context.

Total Capacity:48 GB

Headroom:-330.1 GB

Workstation / DualOOM

Quad RTX 3090/4090 (96GB)

Workstation setup running 70B at FP16 or 123B at Q4.

Total Capacity:96 GB

Headroom:-282.1 GB

Apple SiliconOOM

Mac M3/M4 Pro (36GB)

Unified memory running up to 32B models with generous context.

Total Capacity:36 GB

Headroom:-342.1 GB

Apple SiliconOOM

Mac M3/M4 Pro (48GB)

Unified memory capable of running 70B at Q4_K_M.

Total Capacity:48 GB

Headroom:-330.1 GB

Apple SiliconOOM

Mac M-Max (64GB)

Unified memory running 70B Q5_K_M or 123B Q3.

Total Capacity:64 GB

Headroom:-314.1 GB

Apple SiliconOOM

Mac M-Max (96GB)

Unified memory running 70B FP8/FP16 or Mixtral 8x22B.

Total Capacity:96 GB

Headroom:-282.1 GB

Apple SiliconOOM

Mac M-Max / Ultra (128GB)

Unified memory running 123B FP8 and 70B FP16 with 128k context.

Total Capacity:128 GB

Headroom:-250.1 GB

Apple SiliconOOM

Mac M-Ultra (192GB)

Massive unified memory running DeepSeek 671B MoE quantized.

Total Capacity:192 GB

Headroom:-186.1 GB

Enterprise DatacenterOOM

NVIDIA A100 / H100 (80GB)

Enterprise datacenter GPU running 70B FP8/FP16.

Total Capacity:80 GB

Headroom:-298.1 GB

Enterprise DatacenterOOM

NVIDIA H200 (141GB)

High-capacity HBM3e GPU running 70B FP16 with long context.

Total Capacity:141 GB

Headroom:-237.1 GB

Enterprise DatacenterOOM

NVIDIA B200 (192GB)

Next-gen Blackwell datacenter accelerator with 8TB/s bandwidth.

Total Capacity:192 GB

Headroom:-186.1 GB

Enterprise DatacenterComfortable

8x H100 Cluster (640GB)

Full enterprise rack partition running Llama 3.1 405B or DeepSeek 671B.

Total Capacity:640 GB

Headroom:+261.9 GB

Apple Silicon Note: macOS unified memory is dynamically shared between CPU and GPU. For optimal Ollama/llama.cpp inference, macOS typically allocates up to 75% of total system memory to the GPU by default unless adjusted via sysctl iogpu.wired_mem_limit.

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