HARDWARE FIT CHECK • DeepSeek-R1 Distill Qwen 32B + NVIDIA GeForce RTX 3090 (24GB GDDR6X)25.41 GB / 24 GB VRAM

Can I Run DeepSeek-R1 Distill Qwen 32B on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?

Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running DeepSeek-R1 Distill Qwen 32B (32.8B) on NVIDIA GeForce RTX 3090 (24GB GDDR6X) (24 GB).

! PARTIAL OFFLOAD25.41 GB (106%)~6 t/s

DeepSeek-R1 Distill Qwen 32B on NVIDIA GeForce RTX 3090 (24GB GDDR6X)

! Partial Offload — Slow CPU/RAM Spillover

! PARTIAL OFFLOAD

Required VRAM

25.41 GB

106% of hardware capacity (24 GB)

Est. Decode Speed

~6 tok/s

Dense model @ 936 GB/s

Available Headroom

-1.41 GB

Needs +1.41 GB more VRAM

Memory Allocation & Headroom

25.41 GB allocated of 24 GB capacity

106% utilized

Weights: 19.68 GB

KV: (32K): 4.29 GB

Overhead: 1.44 GB

Deficit: -1.41 GB

Architecture & Throughput Transparency: Single-user decode speed (~6 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 3090 (24GB GDDR6X) (936 GB/s). Prompt prefill / TTFT (~87 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).

Local Hardware Configurations & Pricing

Partial Offload: Dedicated 100% GPU Options

High-bandwidth VRAM specs and current market prices

Consumer GPU✓ Fits Model

32 GB VRAM

NVIDIA GeForce RTX 5090 (32GB GDDR7)

Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization

1792 GB/s575W TDP

$1,999Amazon Prime →

Consumer GPU

24 GB VRAM

NVIDIA GeForce RTX 4090 (24GB GDDR6X)

The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models

1008 GB/s450W TDP

$1,699Amazon Prime →

Multi-GPU Rig✓ Fits Model

48 GB VRAM

2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)

Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0

1648 GB/s700W TDP

$1,550Amazon Prime →

Consumer GPU

24 GB VRAM

NVIDIA GeForce RTX 3090 (24GB GDDR6X)

The #1 budget 24GB card for local LLMs, running 14B–32B models and Q4 70B quantized

936 GB/s350W TDP

$1,499Amazon Prime →

Out of VRAM? Don't downgrade model quality.

Out of VRAM

Spin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (DeepSeek-R1 Distill Qwen 32B @ 25.41 GB VRAM)

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$0.38/hr (RTX 4090 Spot) – $2.39/hr (H100)

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$0.18/hr (RTX 3060) – $0.78/hr (2x RTX 3090)

Rent Spot GPU →

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$2.49/hr (H100) – $19.92/hr (8x H100)

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Quantization Precision Matrix

32K ctx • KV: Q8_0

Quantization	Weights	KV Cache	Total VRAM	Est. Speed	Fit Status
FP1616 bpw	65.6 GB	4.29 GB	72.94 GB (304%)	0 t/s	OOM
Q8_08.5 bpw	34.77 GB	4.29 GB	41.03 GB (171%)	0 t/s	OOM
Q6_K6.56 bpw	26.9 GB	4.29 GB	32.88 GB (137%)	0 t/s	OOM
Q5_K_M5.68 bpw	23.29 GB	4.29 GB	29.15 GB (121%)	~6 t/s	PARTIAL
Q4_K_M4.8 bpw	19.68 GB	4.29 GB	25.41 GB (106%)	~6 t/s	PARTIAL
FP44.25 bpw	17.38 GB	4.29 GB	23.03 GB (96%)	~33 t/s	FITS
IQ3_XS3.3 bpw	13.45 GB	4.29 GB	18.96 GB (79%)	~42 t/s	FITS

FAQ & Reference

Frequently Asked Questions: DeepSeek-R1 Distill Qwen 32B on NVIDIA GeForce RTX 3090 (24GB GDDR6X)

How much VRAM does DeepSeek-R1 Distill Qwen 32B need on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?

At standard 4-bit quantization (Q4_K_M) with 32K context, DeepSeek-R1 Distill Qwen 32B requires 25.41 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will DeepSeek-R1 Distill Qwen 32B achieve on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?

Based on NVIDIA GeForce RTX 3090 (24GB GDDR6X)'s 936 GB/s memory bandwidth, expected decode speed is approximately 6 tokens per second.

Can I expand context beyond 32K tokens?

Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.