DeepSeek-R1 Distill 32B · 10 GPUs measured first-party · llama.cpp Q4_K_M · Updated October 2026

What GPU Do You Need for DeepSeek-R1 Distill 32B?

DeepSeek-R1 Distill 32B is the flagship of the distill family, the closest you get to full R1-style reasoning in a package a 24GB card can hold. Measured on 10 GPUs (llama.cpp, Q4_K_M): 83 tok/s on the B300, ~21GB peak VRAM.

Benchmarked weights: bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF

Fastest we measured

NVIDIA B300

82.9 tok/s on DeepSeek-R1 Distill 32B, the ceiling. Measured on our bench. 288GB of VRAM, $40,000 at launch.

Pros

• 82.9 tok/s on DeepSeek-R1 Distill 32B
• 288GB, clears the DeepSeek-R1 Distill 32B floor
• Rentable by the hour rather than bought

Cons

• 1400W board rating
• Datacenter or workstation hardware, not a retail purchase

Cheapest card that runs it

NVIDIA RTX PRO 6000 Blackwell Workstation Edition

66.06 tok/s on DeepSeek-R1 Distill 32B, lowest launch price that still fits. Measured on our bench. 96GB of VRAM, $8,565 at launch.

Pros

• 66.06 tok/s on DeepSeek-R1 Distill 32B
• 96GB, clears the DeepSeek-R1 Distill 32B floor
• Rentable by the hour rather than bought

Cons

• 600W board rating
• Datacenter or workstation hardware, not a retail purchase

The pairing we actually recommend. Here's the play this model was born for: DeepSeek 32B plus a Qwen 32B-class model on the same prompt, two strong brains from different labs, each double-checking the other. That two-model setup produces higher-quality output than any single model either family offers, and one 24GB card hosts the whole rotation. It's the strongest version of our consensus argument: don't hunt for the one biggest model you can fit; assemble models that are better in different fields and make them audit each other.

Hardware value, measured. Dense 32B inference is bandwidth-bound and single-stream, and the chart shows what that means for spending: the B300 tops out at 83 tok/s while, on the sister dense Qwen3 32B, an RTX 5090 measured 71 tok/s, 85% of the datacenter flagship from a consumer card. Rent big iron for batch serving; for a personal reasoning panel, a 5090, or a 24GB card at half the speed, is the rational buy. Budget for the trace tax too: at 23 tok/s (A10G) a long think is a minute of waiting.

About DeepSeek-R1 Distill 32B. DeepSeek-R1 Distill 32B: from deepseek-ai, 33B parameters, on Hugging Face since January 2025, MIT licence. 426,570 downloads in the last 30 days.

How it compares. H100 80GB HBM3: DeepSeek-R1 Distill 32B 75.8 tok/s, Qwen3-32B 74.07 (33B), Qwen2.5-Coder 32B 75.81 (33B), Qwen2.5-32B 73.5 (33B), Nemotron 3.5 Lightning 30B A3B 323.6 (32B). 2 of 4 beat DeepSeek-R1 Distill 32B here. Nemotron 3.5 Lightning 30B A3B is a mixture-of-experts, so per token it computes only a slice of its size.

Cost on a rented GPU. 1M generated tokens of DeepSeek-R1 Distill 32B: $3.04 on a A100 40GB SXM4 ($0.47/hr, 6.4 hours), $23.25 on a B300 ($6.94/hr, 3.4 hours, 7.6x the cost).

Speed tiers for DeepSeek-R1 Distill 32B. 30+ tok/s: 8 (B300, B200, H100 80GB HBM3); 10-30 tok/s: 2 (A10G, L4). 30 tok/s is roughly where replies outpace reading.

Reading your prompt. Before DeepSeek-R1 Distill 32B writes anything it reads the input: 3428.6 tok/s on the RTX PRO 6000 Blackwell Workstation Edition (1.2s for a 4,000-token prompt), 697.1 on the L4 (5.7s). Long documents and big code files feel this number more than the generation speed.

VRAM for DeepSeek-R1 Distill 32B. Measured peak 19.2GB, so 24GB is the smallest common card size; smallest card it ran on: A10G (24GB). With long context: Q4_K_M 21GB (tested), Q2_K 14GB, Q3_K_M 17GB, Q5_K_M 25GB, Q6_K 29GB.

Power on DeepSeek-R1 Distill 32B. Most efficient: H100 80GB HBM3, 210W, 0.77 kWh per 1M generated tokens. Hungriest: B200, 392W, 1.39 kWh. At $0.15/kWh: $0.12 per 1M generated tokens.

Our verdict

DeepSeek-R1 Distill 32B: 83 tok/s peak, ~21GB floor, and the best reason we know to own a big consumer card, pair it with a Qwen 32B in a two-model consensus and you get output quality neither achieves alone, all on hardware that sits under your desk.

FAQ

What GPU do I need for DeepSeek-R1 Distill 32B?

24GB minimum. Measured peak was ~21GB at Q4_K_M. RX 7900 XTX, RTX 3090/4090, or the 5090 (32GB) for extra context headroom.

What's the recommended two-model setup?

This model plus Qwen3 32B (or QwQ) on the same prompt, answers reconciled. Different labs, different strengths by field, the cross-check catches what each misses. One 24GB card runs them sequentially.

Do I need a datacenter GPU for it?

No. Dense 32B generation is bandwidth-bound: the B300 measured 83 tok/s, while the consumer RTX 5090 hit 71 tok/s on the sister dense 32B, 85% of the output. Datacenter rates only make sense for batch serving.

How bad is the reasoning-token overhead at this size?

Real but worth it: traces run hundreds to thousands of tokens, so at 83 tok/s a hard problem takes tens of seconds of thinking. At this size the trace demonstrably improves answers. This is the tier where you pay the tax and profit.

DeepSeek 32B or QwQ 32B?

Same job, same hardware class, near-identical measured speed (both ~83 tok/s peak). Ideal answer: both, as panel members, two different reasoning lineages disagreeing productively is the whole point of consensus.