BENCHMARK REPORT
Can I Run DeepSeek-V3 (671B Base/Chat) on NVIDIA GeForce RTX 4090?
Comprehensive technical verdict based on static weights, dynamic KV Cache (8k baseline context window), and CUDA runtime overhead.
Official Verdict
NO — CUDA OOM
Executive Hardware Compatibility Summary: DeepSeek-V3 (671B Base/Chat) on NVIDIA GeForce RTX 4090
Authoritative verdict, memory allocation, and token generation performance:
Direct Answer: CUDA OOM — Deficit of 387.2 GBDeepSeek-V3 (671B Base/Chat) exceeds dedicated physical VRAM and triggers PCIe layer offloading on NVIDIA GeForce RTX 4090 (24GB). At standard Q4_K_M 4-bit quantization and an 8,192-token context window, total memory demand is 411.16 GB (408.64 GB weights + 0.57 GB dynamic KV cache + 1.15 GB OS & CUDA buffers). Physical VRAM is exceeded by 387.16 GB, forcing 58 transformer layers to offload across the PCIe bus to host DDR RAM. Estimated generation throughput is ~0.9 tokens/sec (~1111.1 ms per token) with prompt prefill processing reaching ~1873 tok/s.
Run Verdict
CUDA OOM (Offload)
Total Required VRAM
411.16 GB / 24 GB
Decoding Speed
~0.9 tok/s (1111.1ms)
Safe Context Window
Offload to RAM
Hardware Architecture & VRAM Topology
Ada LovelaceNVIDIA GeForce RTX 4090 • 1008 GB/s Bandwidth
Token Generation
~0.9 tok/s(1111.1 ms)
OOM SPILL
VRAM Utilization Breakdown411.16 GB / 24 GB (250%)
VRAM Limit (24G)
408.64G
-387.16G Spill
Weights
408.64 GB
KV Cache
0.57 GB
Activation
0.8 GB
OS/Driver
1.15 GB
Overflow
387.16 GB
Hardware Chokepoint: PCIe Bus Throttling Active
GPU VRAM (24 GB)
3 / 61 Layers Loaded
1008 GB/s Bus
PCIe 4.0 x16 Bottleneck
31.5 GB/s
-97% Bandwidth Drop
Host DDR5 System RAM
58 Layers Offloaded
+446.8 GB RAM Siphoned
Why does your generation speed collapse to ~0.9 tok/s? Because for every single generated token, model weights across the 58 offloaded layers must be transferred across the 31.5 GB/s PCIe slot from host system RAM.
Context Window vs VRAM Scaling Curve
Blue line indicates total VRAM demand across context lengths; Red dashed line is physical VRAM ceiling.
CUDA OOM — Deficit of 387.2 GB
oom58/61 layers will spill to CPU RAM, causing severe PCIe bandwidth throttling (est. 0.9 tok/s).
Throughput
~0.9 tok/s
1111.1 ms / token
Headroom
-387.16 GB
250% of capacity
Layers Fit
3 / 61
58 on DDR5
Max Safe Ctx
0
Tokens before OOM
Host DDR RAM Status:EXCEEDS SYSTEM RAM (446.8 GB required > 32 GB RAM)!
Static Permalink:
Open Static Report/can-i-run/deepseek-v3-671b-on-rtx-4090Ready-to-Run CLI Commands
# Note: Model requires CPU offloading (3/61 layers on GPU)
ollama run deepseek-v3:671bCommands include automatic memory ceiling flags and model layer offload configurations calculated above.
Cloud Hardware Failover
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Hardware Specification Summary
GPU ArchitectureAda Lovelace
Memory Bandwidth1008 GB/s
Model Parameters671B (MoE)
Attention ArchitectureDeepSeek MLA
Prompt Prefill Speed~1873 tok/s (TTFT ~4374ms)
Benchmark Context Tested8,192 tokens
Global Compatibility Matrix for NVIDIA GeForce RTX 4090
Evaluated at Standard Q4_K_M Precision with 8,192 Tokens Context Window
Compatibility Index
75% (43/57)
39 Pass4 Tight14 OOM
| Model & Architecture | Category | Parameters | Required VRAM | Fit Status | Est. Speed | Simulate |
|---|---|---|---|---|---|---|
DeepSeek-R1 (671B Full MoE)MLAMoE DeepSeek | MoE | 671B(37B active) | 411.16 GB | OOM (-387.16G) | ~0.9 tok/s | Simulate |
DeepSeek-V3 (671B Base/Chat)MLAMoE DeepSeek | MoE | 671B(37B active) | 411.16 GB | OOM (-387.16G) | ~0.9 tok/s | Simulate |
DeepSeek-R1-Distill-Llama-70B DeepSeek | LLM | 70.6B | 47.6 GB | OOM (-23.6G) | ~3.4 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-32B DeepSeek | LLM | 32.8B | 23.75 GB | Tight Fit | ~35.3 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-14B DeepSeek | LLM | 14.7B | 11.94 GB | 100% VRAM Fit | ~78.8 tok/s | Simulate |
DeepSeek-R1-Distill-Llama-8B DeepSeek | LLM | 8B | 7.33 GB | 100% VRAM Fit | ~144.8 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-8B DeepSeek | LLM | 8.03B | 7.48 GB | 100% VRAM Fit | ~144.3 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-7B DeepSeek | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~152.5 tok/s | Simulate |
DeepSeek-R1-Distill-Qwen-1.5B DeepSeek | LLM | 1.8B | 2.61 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
DeepSeek-Coder-V2 Lite (16B / 2.4B MoE)MLAMoE DeepSeek | MoE | 15.7B(2.4B active) | 11.21 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
DeepSeek-Coder-V2 (236B / 21B MoE)MLAMoE DeepSeek | MoE | 236B(21B active) | 146.23 GB | OOM (-122.23G) | ~3.7 tok/s | Simulate |
Qwen 2.5 Coder 72B Instruct Qwen | LLM | 72.7B | 48.87 GB | OOM (-24.87G) | ~3.3 tok/s | Simulate |
Qwen 2.5 72B Instruct Qwen | LLM | 72.7B | 48.87 GB | OOM (-24.87G) | ~3.3 tok/s | Simulate |
Qwen 2.5 VL 72B (Vision Flagship) Qwen | LLM | 72.7B | 48.87 GB | OOM (-24.87G) | ~3.3 tok/s | Simulate |
Qwen 2.5 VL 7B (Vision) Qwen | LLM | 7.61B | 6.5 GB | 100% VRAM Fit | ~152.2 tok/s | Simulate |
Qwen 2.5 Coder 32B Instruct Qwen | LLM | 32.8B | 23.75 GB | Tight Fit | ~35.3 tok/s | Simulate |
Qwen 2.5 Coder 14B Instruct Qwen | LLM | 14.7B | 11.94 GB | 100% VRAM Fit | ~78.8 tok/s | Simulate |
Qwen 2.5 Coder 7B Instruct Qwen | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~152.5 tok/s | Simulate |
Qwen 2.5 Coder 1.5B Instruct Qwen | LLM | 1.54B | 2.45 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Qwen 2.5 32B Instruct Qwen | LLM | 32.8B | 23.75 GB | Tight Fit | ~35.3 tok/s | Simulate |
Qwen 2.5 14B Instruct Qwen | LLM | 14.7B | 11.94 GB | 100% VRAM Fit | ~78.8 tok/s | Simulate |
Qwen 2.5 7B Instruct Qwen | LLM | 7.6B | 6.49 GB | 100% VRAM Fit | ~152.5 tok/s | Simulate |
Qwen 2.5 3B Instruct Qwen | LLM | 3.1B | 3.59 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Qwen 2.5 0.5B Instruct Qwen | LLM | 0.49B | 1.8 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Llama 3.3 70B Instruct Llama | LLM | 70.6B | 47.6 GB | OOM (-23.6G) | ~3.4 tok/s | Simulate |
Llama 3.1 70B Instruct Llama | LLM | 70.6B | 47.6 GB | OOM (-23.6G) | ~3.4 tok/s | Simulate |
Llama 3.1 405B Instruct Llama | LLM | 405B | 252.77 GB | OOM (-228.77G) | ~0.6 tok/s | Simulate |
Llama 3.1 8B Instruct Llama | LLM | 8B | 7.33 GB | 100% VRAM Fit | ~144.8 tok/s | Simulate |
Llama 3.2 3B Instruct Llama | LLM | 3.2B | 4.28 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Llama 3.2 1B Instruct Llama | LLM | 1.23B | 2.41 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Llama 3.2 11B Vision Instruct Llama | LLM | 10.6B | 8.92 GB | 100% VRAM Fit | ~109.3 tok/s | Simulate |
Llama 3.2 90B Vision Instruct Llama | LLM | 89B | 59.46 GB | OOM (-35.46G) | ~2.1 tok/s | Simulate |
Microsoft Phi-4 (14B) Phi | LLM | 14.7B | 12.01 GB | 100% VRAM Fit | ~78.8 tok/s | Simulate |
Microsoft Phi-3.5 Mini (3.8B) Phi | LLM | 3.82B | 6.91 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Google Gemma 2 9B Instruct Gemma | LLM | 9.2B | 9.79 GB | 100% VRAM Fit | ~125.9 tok/s | Simulate |
Mistral Small 3 (24B Instruct) Mistral | LLM | 24B | 18.12 GB | 100% VRAM Fit | ~48.3 tok/s | Simulate |
Mistral NeMo (12B Instruct) Mistral | LLM | 12.2B | 10.15 GB | 100% VRAM Fit | ~95 tok/s | Simulate |
Mistral 7B Instruct v0.3 Mistral | LLM | 7.25B | 6.88 GB | 100% VRAM Fit | ~159.8 tok/s | Simulate |
Codestral 22B (Mistral AI) Mistral | LLM | 22.2B | 17.02 GB | 100% VRAM Fit | ~52.2 tok/s | Simulate |
Mistral Large 2 (123B) Mistral | LLM | 123B | 79.77 GB | OOM (-55.77G) | ~1.1 tok/s | Simulate |
Mixtral 8x7B (MoE 47B)MoE Mistral | MoE | 46.7B(12.9B active) | 31.15 GB | OOM (-7.15G) | ~29.7 tok/s | Simulate |
Mixtral 8x22B (MoE 141B)MoE Mistral | MoE | 141B(39B active) | 89.67 GB | OOM (-65.67G) | ~3.3 tok/s | Simulate |
Pixtral 12B (Multimodal) Mistral | LLM | 12.4B | 10.28 GB | 100% VRAM Fit | ~93.4 tok/s | Simulate |
Gemma 2 27B Instruct Gemma | LLM | 27.2B | 21.26 GB | Tight Fit | ~42.6 tok/s | Simulate |
Google Gemma 2 2B Instruct Gemma | LLM | 2.6B | 3.84 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
GLM-4 9B Chat (Zhipu AI) GLM | LLM | 9.4B | 7.79 GB | 100% VRAM Fit | ~123.3 tok/s | Simulate |
FLUX.1 [dev] (12B DiT) FLUX | ImageGen | 12B | 15.68 GB | 100% VRAM Fit | ~96.6 tok/s | Simulate |
FLUX.1 [schnell] (4-step) FLUX | ImageGen | 12B | 15.68 GB | 100% VRAM Fit | ~96.6 tok/s | Simulate |
Stable Diffusion XL (SDXL 1.0) SDXL | ImageGen | 3.5B | 7.51 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Stable Diffusion 1.5 (Classic) SD | ImageGen | 1B | 5.78 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Whisper Large v3 (1.5B) Whisper | Audio | 1.5B | 3.64 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Kokoro 82M (Text-to-Speech) Kokoro | Audio | 0.082B | 1.56 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
Wan2.1 (14B Text-to-Video) Wan | ImageGen | 14.2B | 18.72 GB | 100% VRAM Fit | ~81.6 tok/s | Simulate |
Wan2.1 (1.3B Video) Wan | ImageGen | 1.3B | 7.28 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
HunyuanVideo (13B Video DiT) Hunyuan | ImageGen | 13B | 19.69 GB | 100% VRAM Fit | ~89.1 tok/s | Simulate |
Stable Diffusion 3.5 Large (8B) SD | ImageGen | 8.1B | 13.3 GB | 100% VRAM Fit | ~143 tok/s | Simulate |
BAAI BGE-M3 (Multilingual Embedding) BAAI | LLM | 0.56B | 2.54 GB | 100% VRAM Fit | ~180 tok/s | Simulate |
DeepSeek-V3 (671B Base/Chat) Context Scaling Matrix on NVIDIA GeForce RTX 4090
Dynamic KV Cache footprint and generation throughput measured across sequence lengths (Q4_K_M weights):
| Context Window | KV Cache Memory | Total VRAM Demand | Compatibility Status | Offloaded Layers | Est. Generation Speed |
|---|---|---|---|---|---|
| 2,048 tokens (2k) | 0.14 GB | 410.73 GB / 24 GB | CUDA OOM | 58 layers on RAM | ~0.9 tok/s |
| 4,096 tokens (4k) | 0.28 GB | 410.87 GB / 24 GB | CUDA OOM | 58 layers on RAM | ~0.9 tok/s |
| 8,192 tokens (8k)Standard Baseline | 0.57 GB | 411.16 GB / 24 GB | CUDA OOM | 58 layers on RAM | ~0.9 tok/s |
| 16,384 tokens (16k) | 1.14 GB | 411.73 GB / 24 GB | CUDA OOM | 58 layers on RAM | ~0.9 tok/s |
| 32,768 tokens (32k) | 2.27 GB | 412.86 GB / 24 GB | CUDA OOM | 59 layers on RAM | ~0.7 tok/s |
Hardware Compatibility Q&A: DeepSeek-V3 (671B Base/Chat) on NVIDIA GeForce RTX 4090
Technical answers for memory allocation, CUDA overhead, and execution parameters.
Can NVIDIA GeForce RTX 4090 run DeepSeek-V3 (671B Base/Chat)?
CUDA OOM — Deficit of 387.2 GB. 58/61 layers will spill to CPU RAM, causing severe PCIe bandwidth throttling (est. 0.9 tok/s). At an 8k context window, total VRAM demand is 411.16 GB compared to NVIDIA GeForce RTX 4090's 24 GB frame buffer.
How much VRAM does DeepSeek-V3 (671B Base/Chat) require?
At standard Q4_K_M quantization and 8k context, DeepSeek-V3 (671B Base/Chat) requires approximately 411.16 GB of VRAM. This includes 408.64 GB for model weights, 0.57 GB for dynamic KV cache (compressed using DeepSeek MLA), 0.8 GB for activation buffers, and 1.15 GB for OS display and CUDA runtime overhead.
What generation speed (tokens/sec) can I expect on NVIDIA GeForce RTX 4090?
Estimated autoregressive token generation speed is ~0.9 tokens per second with a per-token latency of ~1111.1 ms, powered by 1008 GB/s memory bandwidth. Prompt prefill processing reaches ~1873 tok/s (TTFT ~4374 ms).
What causes CUDA Out-of-Memory (OOM) when running DeepSeek-V3 (671B Base/Chat)?
CUDA OOM occurs when total memory consumption exceeds NVIDIA GeForce RTX 4090's 24 GB physical VRAM. While model weights stay static, the KV cache grows with every conversation token. When VRAM fills up, inference engines (like Ollama or llama.cpp) must offload remaining layers across the PCIe bus (31.5 GB/s) to host system RAM, causing generation speed to drop sharply.
How do I run DeepSeek-V3 (671B Base/Chat) locally on NVIDIA GeForce RTX 4090?
You can run DeepSeek-V3 (671B Base/Chat) directly with Ollama using "ollama run deepseek-v3-671b". For advanced serving with vLLM or llama.cpp, copy the ready-to-run CLI commands in the CLI Commands section above with optimal context, quantization, and GPU layer offload parameters.
Related Hardware Benchmarks & Model Alternatives
Compare DeepSeek-V3 (671B Base/Chat) and NVIDIA GeForce RTX 4090 against alternative configurations:
Recommended Alternative GPUs for DeepSeek-V3 (671B Base/Chat)
Explore how DeepSeek-V3 (671B Base/Chat) performs across other budget, midrange, and flagship architectures:
Other Popular Models You Can Run on NVIDIA GeForce RTX 4090
Verified open-weights LLMs and image models that fit within NVIDIA GeForce RTX 4090's 24GB VRAM:
Dual GPU Showdown: NVIDIA GeForce RTX 4090 vs NVIDIA GeForce RTX 5090
Compare memory bandwidth (1008 GB/s vs 1792 GB/s), VRAM headroom (24GB vs 32GB), and inference speed head-to-head.
Frequently Asked Questions (FAQ)
Model weights are only one part of total VRAM consumption. When you prompt a model, the KV Cache (Key-Value Cache) stores all historical tokens. At 32k or 64k context, standard GQA attention can demand an additional 4GB to 12GB of VRAM! Combined with activation buffers and OS background consumption (0.8GB on Windows), total memory spikes beyond hardware capacity.