LLMLocal inference
Codestral 22B
Weight estimates and planning VRAM for running Codestral 22B locally at each quantization level. Compare the lowest reference-cost devices that clear the plan.
Q4 plan
15 GB
Q8 plan
28 GB
FP16 plan
51 GB
Context window
32 k tokens
Planning targets add 15% to weight estimates for runtime buffers and a modest KV cache. Long context or a different backend can require more. Official Mistral model card ↗
01 // GPUs that can run Codestral 22B
Compatible hardware by quantization
Sorted by dated reference-cost estimates from Apr 30, 2026. These are not live offers.
Q4Q4_K_M (4-bit)
13GB weights · plan ≥15GBGPUVRAMPriceTier
Q8Q8_0 (8-bit)
24GB weights · plan ≥28GBGPUVRAMPriceTier
FP16FP16 (full precision)
44GB weights · plan ≥51GBGPUVRAMPriceTier
02 // Frequently asked
Codestral 22B GPU questions
How much VRAM does Codestral 22B need?
Codestral 22B uses approximately 13GB for Q4 weights, 24GB at Q8, or 44GB at FP16. GPU Hunter adds 15% planning headroom for runtime buffers and a modest KV cache, producing targets of 15GB, 28GB, and 51GB respectively. Exact memory use varies by backend and context length.
What is the cheapest GPU to run Codestral 22B?
Using GPU Hunter's 15GB Q4 planning target, the lowest reference-cost single device is the Radeon RX 9070 XT (16GB VRAM, dated estimate $549).
Can I run Codestral 22B at FP16?
Potentially. Codestral 22B uses about 44GB for FP16 weights and 51GB under GPU Hunter's planning allowance. Confirm the backend and context requirement before purchasing.
What quantization is best for Codestral 22B?
Q4_K_M uses about 13GB for weights and is the most hardware-accessible option. Q8_0 uses about 24GB and trades more memory for fidelity. FP16 uses about 44GB before runtime and context overhead. The right choice depends on the task, backend, and context window.