HARDWARE BENCHMARK & VRAM ENVELOPE โ€ข CONSUMER GPU24 GB VRAM โ€ข 1008 GB/s

What Models Can Run on NVIDIA GeForce RTX 4090 (24GB GDDR6X)?

24 GB VRAM1008 GB/s Bandwidth450W TDPMSRP: $1,699Cloud: $0.59/hr on RunPod

NVIDIA GeForce RTX 4090 (24GB GDDR6X) provides 24 GB of usable memory with 1008 GB/s memory bandwidth at 450W TDP. The 24GB CUDA enthusiast standard for 14Bโ€“32B Q4_K_M models.

โšก Fits in VRAM (Tight Headroom โ€” Use Q8/Q4 KV Cache)21.93 GB (91%)~40 t/s

Model & Target Hardware

interactive
Agentic Coding, Terminal Workflows & Multi-File Refactoring
Qwen 3.8 27B Instruct on NVIDIA GeForce RTX 4090 (24GB GDDR6X)

โšก Tight Fit โ€” Runs 100% on GPU (Low Headroom)

โšก TIGHT FIT
Required VRAM
21.93 GB
91% of hardware capacity (24 GB)
Est. Decode Speed
~40 tok/s
Dense model @ 1008 GB/s
Available Headroom
+2.07 GB
Safe for OS display & full context
Memory Allocation & Headroom
21.93 GB allocated of 24 GB capacity
91% utilized
Weights: 16.32 GB
KV: (32K): 4.29 GB
Overhead: 1.32 GB
Free Headroom: +2.07 GB
โšก Architecture & Throughput Transparency: Single-user decode speed (~40 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (1008 GB/s). Prompt prefill / TTFT (~580 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 qwen3.8:27b

Quantization Precision Matrix

32K ctx โ€ข KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw54.4 GB4.29 GB61.34 GB (256%)0 t/sOOM
Q8_08.5 bpw28.83 GB4.29 GB34.88 GB (145%)0 t/sOOM
Q6_K6.56 bpw22.3 GB4.29 GB28.12 GB (117%)~6 t/sPARTIAL
Q5_K_M5.68 bpw19.31 GB4.29 GB25.03 GB (104%)~7 t/sPARTIAL
Q4_K_M4.8 bpw16.32 GB4.29 GB21.93 GB (91%)~40 t/sFITS
FP44.25 bpw14.42 GB4.29 GB19.96 GB (83%)~44 t/sFITS
IQ3_XS3.3 bpw11.15 GB4.29 GB16.58 GB (69%)~56 t/sFITS

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All Open-Weights Models Tested on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (Q4_K_M @ 32K Context)

Standard Q4_K_M precision with 32K context window and Q8_0 KV cache

12 Models Benchmarked
ModelArchitectureTotal VRAM NeededHeadroomEst. Decode SpeedCompatibilityDetailed Config
Qwen 3.8 27B Instruct
Alibaba Cloud
27.2B Dense21.93 GB(91%)+2.07 GB~40 tok/sโšก TIGHT FITConfigure CLI โ†’
Qwen 3.6 35B-A3B MoE
Alibaba Cloud
35B MoE24.1 GB(100%)-0.1 GB~68 tok/sโšก PARTIALConfigure CLI โ†’
DeepSeek-V4.1-Flash (552B Engram MoE)
DeepSeek
552B MoE345.59 GB(1440%)-321.59 GB0 (OOM)โœ• OOMConfigure CLI โ†’
Meta Muse Spark Open (816B-A42B MoE)
Meta AI
816B MoE512.86 GB(2137%)-488.86 GB0 (OOM)โœ• OOMConfigure CLI โ†’
Google Gemma 4 26B MoE
Google
26.4B MoE20.23 GB(84%)+3.77 GB~208 tok/sโœ“ FITSConfigure CLI โ†’
Google Gemma 4 E4B (Edge 4B)
Google
4.1B Dense4.24 GB(18%)+19.76 GB~254 tok/sโœ“ FITSConfigure CLI โ†’
DeepSeek-R1 Distill Qwen 32B
DeepSeek
32.8B Dense25.41 GB(106%)-1.41 GB~7 tok/sโšก PARTIALConfigure CLI โ†’
DeepSeek-R1 Distill Qwen 14B
DeepSeek
14.8B Dense13.16 GB(55%)+10.84 GB~71 tok/sโœ“ FITSConfigure CLI โ†’
Meta Llama 4 Scout (109B-A17B MoE)
Meta AI
109B MoE71.66 GB(299%)-47.66 GB0 (OOM)โœ• OOMConfigure CLI โ†’
Meta Llama 3.3 70B Instruct
Meta AI
70.6B Dense49.96 GB(208%)-25.96 GB0 (OOM)โœ• OOMConfigure CLI โ†’
Meta Llama 3.1 8B Instruct
Meta AI
8B Dense7.87 GB(33%)+16.13 GB~128 tok/sโœ“ FITSConfigure CLI โ†’
Microsoft Phi-4 Mini 3.8B
Microsoft
3.8B Dense3.92 GB(16%)+20.08 GB~276 tok/sโœ“ FITSConfigure CLI โ†’