Can I Run Mistral Large 2 (123B Instruct) on AMD Radeon RX 7900 XT (20GB GDDR6)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Mistral Large 2 (123B Instruct) (123B) on AMD Radeon RX 7900 XT (20GB GDDR6) (20 GB).
✕ Out of Memory — Exceeds VRAM Capacity
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run mistral-large:123bLocal Hardware Configurations & Pricing
⚠️ Out of VRAM: High-Capacity Hardware Upgrades2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
Apple Mac Studio M4 Max (128GB Unified Memory)
Running Llama 4 Scout 109B & 70B Q8 locally with 128K+ context
AMD Radeon RX 7900 XT (20GB GDDR6)
Sub-$700 GPU with 20GB VRAM fitting 14B Q8 and 27B Q4 models
⚠️ Out of VRAM? Don't downgrade model quality.
Out of VRAMSpin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (Mistral Large 2 (123B Instruct) @ 83.04 GB VRAM)
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 246 GB | 5.91 GB | 261.27 GB (1306%) | 0 t/s | OOM |
| Q8_08.5 bpw | 130.38 GB | 5.91 GB | 141.6 GB (708%) | 0 t/s | OOM |
| Q6_K6.56 bpw | 100.86 GB | 5.91 GB | 111.05 GB (555%) | 0 t/s | OOM |
| Q5_K_M5.68 bpw | 87.33 GB | 5.91 GB | 97.05 GB (485%) | 0 t/s | OOM |
| Q4_K_M4.8 bpw | 73.8 GB | 5.91 GB | 83.04 GB (415%) | 0 t/s | OOM |
| FP44.25 bpw | 65.19 GB | 5.91 GB | 74.13 GB (371%) | 0 t/s | OOM |
| IQ3_XS3.3 bpw | 50.43 GB | 5.91 GB | 58.86 GB (294%) | 0 t/s | OOM |
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Frequently Asked Questions: Mistral Large 2 (123B Instruct) on AMD Radeon RX 7900 XT (20GB GDDR6)
How much VRAM does Mistral Large 2 (123B Instruct) need on AMD Radeon RX 7900 XT (20GB GDDR6)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Mistral Large 2 (123B Instruct) requires 83.04 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Mistral Large 2 (123B Instruct) achieve on AMD Radeon RX 7900 XT (20GB GDDR6)?
Based on AMD Radeon RX 7900 XT (20GB GDDR6)'s 800 GB/s memory bandwidth, expected decode speed is approximately 0 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.