Model memory guides / deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
deepseek-ai/DeepSeek-R1-Distill-Qwen-14B VRAM estimates
For deepseek-ai/DeepSeek-R1-Distill-Qwen-14B, compare how precision and context change memory planning for text generation.
What changes the memory requirement?
In the Q4_K_M scenario (4.85 effective bits/weight), reported parameters account for approximately 8.34 GiB of weights. At 2,048 tokens, cache and recurrent state add 0.38 GiB. At 32,768 tokens, they add 6.00 GiB. The metadata declares a maximum context of 131,072 tokens; this is not a guarantee of useful output quality at that length.
Reported parameters
14.77 billion
Cache architecture
qwen2
Metadata snapshot
2026-08-31
Weight, cache and planning totals
One sequence, F16 cache, full residency on one device. Context includes both prompt and generated tokens. All memory values are GiB (2³⁰ bytes).
| Precision / effective BPW | Context tokens | Weights | Cache & state | Planning total (+1 GiB budget) | Adjust |
|---|---|---|---|---|---|
| Q4_K_M scenario4.85 bits/weight | 2,048 | 8.34 | 0.38 | 9.71 | Calculator |
| Q4_K_M scenario4.85 bits/weight | 8,192 | 8.34 | 1.50 | 10.84 | Calculator |
| Q4_K_M scenario4.85 bits/weight | 32,768 | 8.34 | 6.00 | 15.34 | Calculator |
| Q8_0 scenario8.5 bits/weight | 2,048 | 14.62 | 0.38 | 15.99 | Calculator |
| Q8_0 scenario8.5 bits/weight | 8,192 | 14.62 | 1.50 | 17.12 | Calculator |
| Q8_0 scenario8.5 bits/weight | 32,768 | 14.62 | 6.00 | 21.62 | Calculator |
| 16-bit scenario16 bits/weight | 2,048 | 27.51 | 0.38 | 28.89 | Calculator |
| 16-bit scenario16 bits/weight | 8,192 | 27.51 | 1.50 | 30.01 | Calculator |
| 16-bit scenario16 bits/weight | 32,768 | 27.51 | 6.00 | 34.51 | Calculator |
Q4_K_M and Q8_0 here are assumed effective-BPW scenarios, not inspected GGUF files. Tensor mixtures and conversions can change actual weight storage. The calculator lets you inspect an actual artifact and choose your GPU before presenting a personalized result.
How these numbers are calculated
Weights = reported parameter count × effective bits per weight ÷ 8. Cache and state use the architecture-specific equations in the same engine as our calculator. Planning total = weights + cache/state + the stated runtime budget. For MoE, full residency includes all experts, not just active parameters per token.
Profile: llama.cpp / CUDA, Flash Attention enabled, micro-batch 512, prompt batch 2,048, no recurrent rollback snapshots. Engine 3.5.0; pinned runtime source. Multi-GPU, partial offload, training, speculative decoding and image/audio processing are outside these scenarios.
Model-specific assumptions and limitations
- head_dim uses the documented architecture default (128).
- Weights use parameter count × assumed effective BPW; mixed tensor types and conversion can change artifact size.
- Runtime allowance is an editable planning budget, not a measured compute-buffer prediction or an error bound.
- No model execution or hardware compatibility is certified. Available device memory may be lower than listed capacity.
Sources and reproducibility
Configuration at the pinned model revision · Model repository at that revision · Equations and external evidence
The parameter count and configuration come from our bundled metadata snapshot. This page is generated from that snapshot; it does not claim a live metadata check or a GPU benchmark.
Revision: 1df8507178afcc1bef68cd8c393f61a886323761. Open a table scenario to inspect that revision in the calculator.
Compare other models
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