HARDWARE FIT CHECK • Qwen 3.6 35B-A3B MoE + NVIDIA GeForce RTX 4090 (24GB GDDR6X)24.1 GB / 24 GB VRAM

Can I Run Qwen 3.6 35B-A3B MoE on NVIDIA GeForce RTX 4090 (24GB GDDR6X)?

Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Qwen 3.6 35B-A3B MoE (35B) on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (24 GB).

⚠️ Exceeds VRAM — Requires Partial CPU/RAM Offload (Slow)24.1 GB (100%)~68 t/s

Model & Target Hardware

interactive
Ultra-Fast 100+ tok/s Coding & Local RAG on 24GB GPUs
Qwen 3.6 35B-A3B MoE on NVIDIA GeForce RTX 4090 (24GB GDDR6X)

⚠️ Partial Offload — Slow CPU/RAM Spillover

⚡ PARTIAL OFFLOAD
Required VRAM
24.1 GB
100% of hardware capacity (24 GB)
Est. Decode Speed
~68 tok/s
3B active params @ 1008 GB/s
Available Headroom
-0.1 GB
Needs +0.1 GB more VRAM
Memory Allocation & Headroom
24.1 GB allocated of 24 GB capacity
100% utilized
Weights: 21 GB
KV: (32K): 1.61 GB
Overhead: 1.49 GB
Deficit: -0.1 GB
⚡ Architecture & Throughput Transparency: Single-user decode speed (~68 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 4090 (24GB GDDR6X) (1008 GB/s). Prompt prefill / TTFT (~986 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 qwen3.6:35b-a3b

Quantization Precision Matrix

32K ctx • KV: Q8_0
QuantizationWeightsKV CacheTotal VRAMEst. SpeedFit Status
FP1616 bpw70 GB1.61 GB74.81 GB (312%)0 t/sOOM
Q8_08.5 bpw37.1 GB1.61 GB40.76 GB (170%)0 t/sOOM
Q6_K6.56 bpw28.7 GB1.61 GB32.06 GB (134%)~53 t/sPARTIAL
Q5_K_M5.68 bpw24.85 GB1.61 GB28.08 GB (117%)~60 t/sPARTIAL
Q4_K_M4.8 bpw21 GB1.61 GB24.1 GB (100%)~68 t/sPARTIAL
FP44.25 bpw18.55 GB1.61 GB21.56 GB (90%)~344 t/sFITS
IQ3_XS3.3 bpw14.35 GB1.61 GB17.21 GB (72%)~420 t/sFITS

Local Hardware Configurations & Pricing

High-bandwidth VRAM specs and current market prices
Consumer 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
Consumer GPU32 GB VRAM

NVIDIA GeForce RTX 5090 (32GB GDDR7)

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

1792 GB/s575W TDP
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
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

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