RTX 4090

NVIDIA GeForce RTX 40 (Ada Lovelace)

VRAM
24 GB
Memory bandwidth
1,008 GB/s
fp16 compute
330.3 TFLOPS
Class
Consumer

Figures assume this GPU plus 32 GB of system RAM (a typical desktop pairing). "What runs on it" is judged at a 8,192-token context. Speeds are estimates, not measurements.

What runs on it

Model Sweet-spot quant Est. speed Community
DeepSeek-R1-Distill-Llama-8B deepseek-ai Q8_0 Runs fully on GPU @ 8K ctx 63–84 tok/s (estimate) no community data
DeepSeek-R1-Distill-Qwen-1.5B deepseek-ai Q8_0 Runs fully on GPU @ 8K ctx 284–379 tok/s (estimate) no community data
DeepSeek-R1-Distill-Qwen-14B deepseek-ai Q8_0 Runs fully on GPU @ 8K ctx 35–47 tok/s (estimate) no community data
DeepSeek-R1-Distill-Qwen-32B deepseek-ai Q4_K_M Runs fully on GPU @ 8K ctx 27–37 tok/s (estimate) no community data
DeepSeek-R1-Distill-Qwen-7B deepseek-ai Q8_0 Runs fully on GPU @ 8K ctx 71–94 tok/s (estimate) no community data
Devstral-Small-2-24B-Instruct-2512 mistralai Q4_K_M Runs fully on GPU @ 8K ctx 39–51 tok/s (estimate) no community data
GLM-4.7-Flash zai-org Q5_K_M Runs fully on GPU @ 8K ctx no community data
Kimi-VL-A3B-Instruct moonshotai Q8_0 Runs fully on GPU @ 8K ctx no community data
Llama-3.1-8B-Instruct meta-llama Q8_0 Runs fully on GPU @ 8K ctx 63–84 tok/s (estimate) no community data
Mistral-Small-3.2-24B-Instruct-2506 mistralai Q4_K_M Runs fully on GPU @ 8K ctx 39–51 tok/s (estimate) no community data
Phi-4-mini-instruct microsoft Q8_0 Runs fully on GPU @ 8K ctx 117–156 tok/s (estimate) no community data
Phi-4-reasoning microsoft Q8_0 Runs fully on GPU @ 8K ctx 35–47 tok/s (estimate) no community data
Qwen2.5-7B-Instruct Qwen Q8_0 Runs fully on GPU @ 8K ctx 71–94 tok/s (estimate) no community data
Qwen2.5-Omni-7B Qwen BF16 Runs fully on GPU @ 8K ctx 26–35 tok/s (estimate) no community data
Qwen2.5-VL-32B-Instruct Qwen Q4_K_M Runs fully on GPU @ 8K ctx 27–37 tok/s (estimate) no community data
Qwen3-14B Qwen Q8_0 Runs fully on GPU @ 8K ctx 35–47 tok/s (estimate) no community data
Qwen3-30B-A3B-Instruct-2507 Qwen Q4_K_M Runs fully on GPU @ 8K ctx no community data
Qwen3-32B Qwen Q4_K_M Runs fully on GPU @ 8K ctx 28–37 tok/s (estimate) no community data
Qwen3-8B Qwen Q8_0 Runs fully on GPU @ 8K ctx 61–81 tok/s (estimate) no community data
Qwen3-Coder-30B-A3B-Instruct Qwen Q4_K_M Runs fully on GPU @ 8K ctx no community data
Qwen3-Embedding-0.6B Qwen Q8_0 Runs fully on GPU @ 8K ctx 383–511 tok/s (estimate) no community data
Qwen3-Embedding-4B Qwen Q4_K_M Runs fully on GPU @ 8K ctx 163–218 tok/s (estimate) no community data
Qwen3-Embedding-8B Qwen Q4_K_M Runs fully on GPU @ 8K ctx 103–137 tok/s (estimate) no community data
Qwen3-Omni-30B-A3B-Instruct Qwen Q4_K_M Runs fully on GPU @ 8K ctx no community data
Qwen3-Reranker-0.6B Qwen BF16 Runs fully on GPU @ 8K ctx 284–378 tok/s (estimate) no community data
Qwen3-Reranker-4B Qwen BF16 Runs fully on GPU @ 8K ctx 65–87 tok/s (estimate) no community data
Qwen3-Reranker-8B Qwen BF16 Runs fully on GPU @ 8K ctx 34–46 tok/s (estimate) no community data
Qwen3.6-27B Qwen Q4_K_M Runs fully on GPU @ 8K ctx 32–43 tok/s (estimate) no community data
Qwen3.6-35B-A3B Qwen Q4_K_M Runs fully on GPU @ 8K ctx no community data
SmolLM3-3B HuggingFaceTB BF16 Runs fully on GPU @ 8K ctx 89–119 tok/s (estimate) no community data
dots.ocr rednote-hilab BF16 Runs fully on GPU @ 8K ctx 96–128 tok/s (estimate) no community data
gemma-4-12B-it google Q8_0 Runs fully on GPU @ 8K ctx 45–60 tok/s (estimate) no community data
gemma-4-26B-A4B-it google Q4_K_M Runs fully on GPU @ 8K ctx no community data
gemma-4-31B-it google Q4_K_M Runs fully on GPU @ 8K ctx 29–39 tok/s (estimate) no community data
gemma-4-E2B-it google Q8_0 Runs fully on GPU @ 8K ctx 118–157 tok/s (estimate) no community data
gemma-4-E4B-it google Q8_0 Runs fully on GPU @ 8K ctx 72–97 tok/s (estimate) no community data
gpt-oss-20b openai F16 Runs fully on GPU @ 8K ctx no community data
phi-4 microsoft Q8_0 Runs fully on GPU @ 8K ctx 35–47 tok/s (estimate) no community data
Hunyuan-A13B-Instruct tencent Q4_K_M CPU offload @ 8K ctx · ~13 GPU layers no community data
Kimi-Dev-72B moonshotai IQ4_XS CPU offload @ 8K ctx · ~39 GPU layers 2 tok/s (estimate) no community data
Llama-3.3-70B-Instruct meta-llama Q4_K_M CPU offload @ 8K ctx · ~36 GPU layers 2 tok/s (estimate) no community data

"Est. speed" is a modelled range labelled estimate (D8) for generation (decode) throughput. "Community" shows the median of approved user-submitted reports on this GPU class only where enough exist — never an estimate. "pp" is measured prompt-processing (ingestion) throughput from approved community reports; rows without a measurement show none.

What can I run on a RTX 4090?

On this GPU, 12 catalog models run fully on the GPU at an 8,192-token context. The most capable is Qwen3.6-35B-A3B at Q4_K_M (needs ~23.9 GiB). Pick a smaller model or a lower quant for more headroom.

Biggest model: Qwen3.6-35B-A3B at Q4_K_M 

llama-server -m Qwen3.6-35B-A3B-UD-Q4_K_M.gguf -c 8192 -ngl 999

Derived from the fit engine at an 8,192-token context. See more answer packs.

Add a second RTX 4090?

A second RTX 4090 pools VRAM: 2 × 24 GB = 48 GB combined. Bigger models can then load because their weights split across both cards — but a second card does not make generation proportionally faster (see the reality check below).

A second RTX 4090 adds 24 GB of VRAM for about $2,250 — ≈$93.75/GB of added VRAM (used price as of 2026-07-18 — BestValueGPU).

3 more catalog models could newly fit fully in the combined 48 GB at a 8,192-token context — for example:

Estimate — assumes the model's weights split across both cards (a layer split, as llama.cpp does by default). This is a combined-VRAM projection, not a measured or verdict-chipped result: the fit engine treats two cards as one summed memory pool and does not model the link between them. Check your exact model and context in the calculator.

The honest reality of a second card

  • Bandwidth doesn't add. Two cards give more VRAM, not more memory bandwidth per token. Token generation is bandwidth-bound, so a layer-split model decodes at roughly one card's speed — not double.
  • Layer-split vs tensor-parallel. The common desktop setup (llama.cpp) splits layers across cards and runs them in sequence, so one GPU works at a time. True tensor-parallel serving (e.g. vLLM) can use both at once, but wants matched cards and a fast interconnect.
  • pp vs tg. Prompt processing (pp) can gain more from a second card than token generation (tg); raw decode throughput barely moves. Don't expect a 2× tok/s jump.
  • PCIe / NUMA. Cards talk over PCIe (or NVLink where supported), far slower than on-card VRAM. A layer split crosses it about once per token so the hit is small; tensor-parallel crosses it constantly, and cards on different CPU sockets (NUMA) add latency.
  • Power & PSU. A second card roughly doubles GPU power draw — check PSU headroom, connectors, slot spacing and airflow before buying.

Read: Multi-GPU for local LLMs — when a second card is worth it →

Derived from the fit engine at a 8,192-token context, comparing a single 24 GB card against a summed 48 GB two-card pool.

Price history

Prices are point-in-time observations, not live quotes.

new

  • Oct 2022 · $1,599 new — VideoCardz launch report

Not enough history yet — trends appear once at least three dated observations are recorded.

used

  • Jul 2026 · $2,250 used — BestValueGPU

Not enough history yet — trends appear once at least three dated observations are recorded.

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