How much VRAM does Yi VL 34B Chat need?
Yi VL 34B Chat needs about 21.3 GB of VRAM at Q4_K_M, or 36.7 GB at Q8_0 and 68.4 GB at FP16. That fits a consumer card: the smallest in our set that holds it at Q4_K_M is the RTX 3090 (24 GB). Weights plus runtime overhead; with only 4K of context the cache stays small.
Weights-plus-runtime footprint across the six most common quantizations. KV cache is context-dependent and comes on top. The 4K window is short, so weights dominate the budget; size it precisely in the GPU Memory Calculator.
Can your GPU run Yi VL 34B Chat?
Pick your card — the answer below is computed by the same engine that produces every figure on this page.
RTX 5090 runs Yi VL 34B Chat at Q4_K_M with 9.1 GB to spare; room left for context and everyday runtime pressure.
Weights plus runtime overhead. KV cache is not included — it depends on context length and this model’s published architecture.
| Weights (Q4_K_M) | 19.5 GB |
| Runtime overhead | 1.7 GB |
| Estimated total | 21.3 GB |
| Usable VRAM (95% of 32 GB) | 30.4 GB |
Across every GPU we track
- RTX 3060 12GBNot enough-9.9 GB
- RTX 4060 Ti 16GBNot enough-6.1 GB
- RTX 4070 Ti SUPERNot enough-6.1 GB
- RTX 3090Tight+1.5 GB
- RTX 4090Tight+1.5 GB
- RTX 5090Recommended+9.1 GB
- A100 40GBExcellent+16.7 GB
- RTX A6000Excellent+24.3 GB
- L40SExcellent+24.3 GB
- A100 80GBExcellent+54.7 GB
- H100 80GBExcellent+54.7 GB
- H200 141GBExcellent+112.7 GB
Estimates, not guarantees: real usage moves with runtime, driver, batch size and context. Size a specific context window in the GPU Memory Calculator.
How was this number calculated? Every figure here comes from Bitpute’s documented calculation methodology — parameters, precision, quantization, runtime overhead and usable VRAM, each formula written out in full.
Memory by quantization
| Quant | Weights | + runtime overhead |
|---|---|---|
| FP16 | 64.4 GB | 68.4 GB |
| Q8_0 | 34.2 GB | 36.7 GB |
| Q6_K | 26.4 GB | 28.5 GB |
| Q5_K_M | 22.9 GB | 24.8 GB |
| Q4_K_M | 19.5 GB | 21.3 GB |
| Q4_0 | 18.1 GB | 19.8 GB |
Overhead = 0.75 GB + 5% of weights (CUDA context, buffers). Add KV cache on top. With only 4K of context, weights dominate the budget.
Which quantization should you actually run?
Yi VL 34B Chat is too large for a 12–16 GB card. On RTX 4090, Q4_K_M (21.3 GB) fits though context headroom gets tight — the realistic single-GPU entry point. Higher quality (Q6_K 28.5 GB, FP16 68.4 GB) needs RTX A6000 or multiple GPUs — for occasional use, renting is usually cheaper than buying. The vision encoder adds a few hundred MB on top of these text-weight figures.
Single-GPU fit at Q4_K_M (21.3 GB + KV)
RTX 3060 12GBRTX 4060 Ti 16GBRTX 4070 Ti SUPERRTX 3090RTX 4090RTX 5090A100 40GBRTX A6000L40SA100 80GBH100 80GBH200 141GB
Where the memory goes
Weights and overhead are exact for Q4_K_M. The KV bar is a generic 32-layer transformer at 8K; at this scale the weights dominate, but long context still adds gigabytes — size it in the calculator is for.
Weighing the cheapest card that fits against the next one up? RTX 3090 vs RTX 5090 compares them on bandwidth, power and which models each one holds.
Common questions
How much VRAM does Yi VL 34B Chat need?
Yi VL 34B Chat needs about 21.3 GB of VRAM at Q4_K_M, 36.7 GB at Q8_0, or 68.4 GB at FP16. That is weights plus about 0.75 GB of runtime overhead and 5% of weight size; KV cache is additional and depends on context length.
What GPU can run Yi VL 34B Chat?
At Q4_K_M the smallest card in our set that fits is the RTX 3090 (24 GB usable at 95%). At FP16 you need the A100 80GB (80 GB) or larger.
Can Yi VL 34B Chat run on 24 GB?
Yes. At Q4_K_M it needs about 21.3 GB, which fits inside the ~22.8 GB usable on a 24 GB card.