✅ RUNS NATIVE FP16 & ALL QUANTIZATIONS
Mistral-Large-2-123B on 4x NVIDIA A100 80GB (320GB) (320GB VRAM)
In 4-bit Q4_K_M, Mistral-Large-2-123B uses only 80.23 GB of your 320 GB VRAM (25% utilization) at ~17 tokens/sec. In full 16-bit precision, it requires 256.8 GB.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
80.23 GB / 320 GB
🟦 Weights (76.81 GB)
🟪 KV Cache (2.75 GB)
🟨 Activation Buffer (0.67 GB)
Total Required VRAM80.23 GB
Available Hardware VRAM320 GB (4x NVIDIA A100 80GB (320GB))
VRAM Headroom / Overflow239.77 GB
Estimated Generation Speed~17 tokens/sec
Architecture & AttentionDense-GQA (88L, 8 KV heads)
🚀 Ready-to-Run Launch Command
ollama run mistral-large:123b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf mistralai/Mistral-Large-Instruct-2411-GGUF -c 8192 -ngl 99 -fa
CLOUD GPU ALTERNATIVE
Need 80.23 GB+ VRAM to run Mistral-Large-2-123B?
Recommended cloud setup for 4-bit: Multi-GPU H100 / H200 Node (or 256.8 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:
| Provider | Best For | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Bonus / Trial | Action |
|---|---|---|---|---|---|---|
| RunPod | 1-Click vLLM / Ollama Pods | $0.44 / hr | $1.39 / hr | $2.39 / hr | Instant PyTorch / vLLM Pods + Serverless GPU | Deploy on RunPod → |
| Vast.ai | Cheapest Spot GPU Market | $0.29 / hr | $0.95 / hr | $1.79 / hr | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Check Spot GPUs → |
| Vultr Cloud GPU | Enterprise SLA & Bare Metal | — | $1.79 / hr | $2.69 / hr | $300 Free Credit for New Users | Claim $300 Credit → |
| DigitalOcean GPU Droplets | Managed H100 Droplets | — | — | $2.99 / hr | $200 Free Trial (60 Days) | Get $200 Free → |
All Quantization Formats: Mistral-Large-2-123B on 4x NVIDIA A100 80GB (320GB)
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x 4x NVIDIA A100 80GB (320GB) (320GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 253.38 GB | 2.75 GB | 256.8 GB | Yes (80%) | ~5.1 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 134.61 GB | 2.75 GB | 138.03 GB | Yes (43%) | ~9.7 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 134.61 GB | 2.75 GB | 138.03 GB | Yes (43%) | ~9.7 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q6_K (6-bit) | 6.56 bpw | 103.89 GB | 2.75 GB | 107.31 GB | Yes (34%) | ~12.5 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 90.11 GB | 2.75 GB | 93.53 GB | Yes (29%) | ~14.5 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 76.81 GB | 2.75 GB | 80.23 GB | Yes (25%) | ~17 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 67.3 GB | 2.75 GB | 70.73 GB | Yes (22%) | ~19.3 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 61.92 GB | 2.75 GB | 65.34 GB | Yes (20%) | ~21 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
Frequently Asked Questions (Mistral-Large-2-123B + 4x NVIDIA A100 80GB (320GB))
Can 4x NVIDIA A100 80GB (320GB) (320GB) run Mistral-Large-2-123B locally?
Yes — 4x NVIDIA A100 80GB (320GB) (320GB) easily runs Mistral-Large-2-123B even in full FP16. In 4-bit Q4_K_M, Mistral-Large-2-123B uses only 80.23 GB of your 320 GB VRAM (25% utilization) at ~17 tokens/sec. In full 16-bit precision, it requires 256.8 GB.
How is KV Cache calculated for Mistral-Large-2-123B?
Mistral-Large-2-123B uses Dense-GQA with 88 transformer layers, 96 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 2.75 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 1.38 GB.
Compare Mistral-Large-2-123B on Other GPUs
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