How much VRAM does Whisper Large v3 need?
Whisper Large v3 needs about 1.7 GB of VRAM at Q4_K_M, or 2.4 GB at Q8_0 and 3.8 GB at FP16. That fits a consumer card: the smallest in our set that holds it at Q4_K_M is the RTX 3060 12GB (12 GB). Weights plus runtime overhead; KV cache for your context length is on top.
Weights-plus-runtime footprint across the six most common quantizations. KV cache is context-dependent and comes on top. Size it for your context in the GPU Memory Calculator.
Can your GPU run Whisper Large v3?
Pick your card — the answer below is computed by the same engine that produces every figure on this page.
RTX 3060 12GB runs Whisper Large v3 at Q4_K_M with 9.7 GB to spare; plenty of room for a long context window or a second model alongside it.
Weights plus runtime overhead. KV cache is not included — it depends on context length and this model’s published architecture.
| Weights (Q4_K_M) | 0.9 GB |
| Runtime overhead | 0.8 GB |
| Estimated total | 1.7 GB |
| Usable VRAM (95% of 12 GB) | 11.4 GB |
Across every GPU we track
- RTX 3060 12GBExcellent+9.7 GB
- RTX 4060 Ti 16GBExcellent+13.5 GB
- RTX 4070 Ti SUPERExcellent+13.5 GB
- RTX 3090Excellent+21.1 GB
- RTX 4090Excellent+21.1 GB
- RTX 5090Excellent+28.7 GB
- A100 40GBExcellent+36.3 GB
- RTX A6000Excellent+43.9 GB
- L40SExcellent+43.9 GB
- A100 80GBExcellent+74.3 GB
- H100 80GBExcellent+74.3 GB
- H200 141GBExcellent+132.3 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 | 2.9 GB | 3.8 GB |
| Q8_0 | 1.5 GB | 2.4 GB |
| Q6_K | 1.2 GB | 2.0 GB |
| Q5_K_M | 1.0 GB | 1.8 GB |
| Q4_K_M | 0.9 GB | 1.7 GB |
| Q4_0 | 0.8 GB | 1.6 GB |
Overhead = 0.75 GB + 5% of weights (CUDA context, buffers). Add KV cache on top; size it for your context length.
Which quantization should you actually run?
Whisper-class audio models are tiny — about 3.8 GB at FP16 — so they run on modest GPUs or even CPU. There is little reason to quantize; pick the model size that matches the accuracy and speed you need.
Single-GPU fit at Q4_K_M (1.7 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 — on a model this small it is the term that decides whether you fit, so check yours in the calculator is for.
Weighing the cheapest card that fits against the next one up? RTX 3060 12GB vs RTX 4060 Ti 16GB compares them on bandwidth, power and which models each one holds.
Common questions
How much VRAM does Whisper Large v3 need?
Whisper Large v3 needs about 1.7 GB of VRAM at Q4_K_M, 2.4 GB at Q8_0, or 3.8 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 Whisper Large v3?
At Q4_K_M the smallest card in our set that fits is the RTX 3060 12GB (12 GB usable at 95%). At FP16 you need the RTX 3060 12GB (12 GB) or larger.
Can Whisper Large v3 run on 24 GB?
Yes. At Q4_K_M it needs about 1.7 GB, which fits inside the ~22.8 GB usable on a 24 GB card.
Same memory footprint as 1 other checkpoint
Whisper Large v3 has an identical parameter count to this, so every figure on this page applies to it unchanged. They are separate releases at the same size, so the memory budget is identical — check each model card for capability differences.
More in the Whisper family
Whisper TinyWhisper BaseWhisper SmallWhisper MediumWhisper Large v3 Turbo v3Whisper Large v2