Will It Fit?
Estimate text inference VRAM across Transformers and vLLM with a compact, explainable breakdown.
Model pages and release notes are included here too.
DeepSeek R1 Distill Qwen 32B · vLLM · Official BF16 checkpoint
Required GPU VRAM (0.9 budget)
41.4 GB
Core estimate: 37.2 GB. Against RTX 4090 24GB, this leaves 15.6 GB of deficit.
At 4,096 tokens, the estimated max concurrency is 0 concurrent requests.
Quick read
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Nominal VRAM
Core estimate
Required GPU VRAM (0.9 budget)
Deficit
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What fits this setup
Breakdown
Where the memory goes
Weights
32B resident parameters at 1.02 bytes each. Calibrated from the official checkpoint profile.
32.8 GB
KV cache
Concurrency 1, context 4,096, 64 KV-bearing layers, 8 KV heads, BF16 cache storage.
1.1 GB
Runtime / safety overhead
Conservative buffer for allocator fragmentation, kernels, and runtime scratch space.
3.4 GB
Weights = parameter count × bytes per parameter.
KV cache grows with context length, KV-bearing layers, concurrent requests, and the selected KV cache dtype.
Show the substituted formulas
Weights
parameter count × bytes per weight
32B × 1.02 = 32.8 GB
The official DeepSeek-R1-Distill-Qwen-32B checkpoint totals about 32.76 GB on Hugging Face.
KV cache
batch × effective KV tokens across attention layers × 2 × KV heads × head dim × bytes per KV element
1 × 262,144 × 2 × 8 × 128 × 2 = 1.1 GB
Longer context, deeper models, and larger batches all expand the cache linearly. BF16 controls the bytes per stored KV element.
Overhead
max(1.5 GB, 10% of weights + KV cache + linear state)
max(1.5 GB, 10% of 33.8 GB) = 3.4 GB
This leaves room for runtime buffers instead of claiming an unrealistically exact fit.
Model
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DeepSeek R1 Distill Qwen 32B
32B dense • 131,072 context • 8 KV heads
Total params
Active params
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Hidden size
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KV heads
KV-bearing layers