HARDWARE FIT CHECK • DeepSeek-R1 Distill Qwen 32B + NVIDIA GeForce RTX 4090 (24GB GDDR6X)25.41 GB / 24 GB VRAM
Can I Run DeepSeek-R1 Distill Qwen 32B on NVIDIA GeForce RTX 4090 (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 4090 (24GB GDDR6X) (24 GB).
⚠️ Exceeds VRAM — Requires Partial CPU/RAM Offload (Slow)25.41 GB (106%)~7 t/s
DeepSeek-R1 Distill Qwen 32B on NVIDIA GeForce RTX 4090 (24GB GDDR6X)
⚠️ Partial Offload — Slow CPU/RAM Spillover
⚡ PARTIAL OFFLOAD
Required VRAM
25.41 GB
106% of hardware capacity (24 GB)Est. Decode Speed
~7 tok/s
Dense model @ 1008 GB/sAvailable Headroom
-1.41 GB
Needs +1.41 GB more VRAMMemory 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 (~7 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (1008 GB/s). Prompt prefill / TTFT (~102 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama run deepseek-r1:32b⚠️ Out of VRAM? Don't downgrade model quality.
Out of VRAMSpin 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)
RunPod Cloud GPUsBest 1-Click Ollama & vLLM
$0.34/hr (RTX 4070 Ti) – $2.39/hr (H100)Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090
$0.18/hr (RTX 3060) – $0.55/hr (RTX 4090)Lambda GPU CloudEnterprise 8x H100 Clusters
$1.99/hr (GH200 / H100)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%) | ~7 t/s | PARTIAL |
| FP44.25 bpw | 17.38 GB | 4.29 GB | 23.03 GB (96%) | ~37 t/s | FITS |
| IQ3_XS3.3 bpw | 13.45 GB | 4.29 GB | 18.96 GB (79%) | ~47 t/s | FITS |
Local Hardware Configurations & Pricing
High-bandwidth VRAM specs and current market pricesConsumer GPU24 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 →
Consumer GPU32 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 →
Multi-GPU Rig48 GB VRAM
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for 70B Q4_K_M & Qwen 3.8 27B Q8
936 GB/s700W TDP
$1,550Amazon Prime →
Consumer GPU16 GB VRAM
NVIDIA GeForce RTX 5080 (16GB GDDR7)
Blazing fast 8B–14B coding models with FP4/Q6_K precision
960 GB/s360W TDP
$999Amazon Prime →
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