HARDWARE FIT CHECK • Qwen 3.6 35B-A3B MoE + NVIDIA GeForce RTX 3090 (24GB GDDR6X)24.1 GB / 24 GB VRAM

Can I Run Qwen 3.6 35B-A3B MoE on NVIDIA GeForce RTX 3090 (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 3090 (24GB GDDR6X) (24 GB).

! PARTIAL OFFLOAD24.1 GB (100%)~61 t/s

Qwen 3.6 35B-A3B MoE on NVIDIA GeForce RTX 3090 (24GB GDDR6X)MoE (3B Active / 35B Total)

! Partial Offload — Slow CPU/RAM Spillover

! PARTIAL OFFLOAD

Required VRAM

24.1 GB

100% of hardware capacity (24 GB)

Est. Decode Speed

~61 tok/s

3B active params @ 936 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 GBMoE (3B Active)

KV: (32K): 1.61 GB

Overhead: 1.49 GB

Deficit: -0.1 GB

Architecture & Throughput Transparency: Single-user decode speed (~61 tok/s) is memory-bandwidth bound at batch size = 1 on NVIDIA GeForce RTX 3090 (24GB GDDR6X) (936 GB/s). Prompt prefill / TTFT (~885 tok/s) is compute-bound and utilizes GPU Tensor cores. Actual latency varies with FlashAttention-2 and runtime engine (Ollama/vLLM/llama.cpp).

MoE Memory vs Speed Architecture: While decode throughput scales with the active parameter count (3B parameters streamed per token), all 35B parameters must be resident in VRAM so the dynamic router can route tokens to any expert layer.

Local Hardware Configurations & Pricing

Partial Offload: Dedicated 100% GPU Options

High-bandwidth VRAM specs and current market prices

Consumer GPU✓ Fits Model

32 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 →

Consumer GPU

24 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 →

Multi-GPU Rig✓ Fits Model

48 GB VRAM

2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)

Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0

1648 GB/s700W TDP

$1,550Amazon Prime →

Consumer GPU

24 GB VRAM

NVIDIA GeForce RTX 3090 (24GB GDDR6X)

The #1 budget 24GB card for local LLMs, running 14B–32B models and Q4 70B quantized

936 GB/s350W TDP

$1,499Amazon Prime →

Out of VRAM? Don't downgrade model quality.

Out of VRAM

Spin up an on-demand cloud GPU instance in 60 seconds with pre-installed Ollama and vLLM. (Qwen 3.6 35B-A3B MoE @ 24.1 GB VRAM)

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$0.38/hr (RTX 4090 Spot) – $2.39/hr (H100)

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Vast.ai GPU MarketplaceCheapest Dual RTX 3090 / 4090

$0.18/hr (RTX 3060) – $0.78/hr (2x RTX 3090)

Rent Spot GPU →

Lambda GPU CloudEnterprise 8x H100 Clusters

$2.49/hr (H100) – $19.92/hr (8x H100)

Check H100 Availability →

Quantization Precision Matrix

32K ctx • KV: Q8_0

Quantization	Weights	KV Cache	Total VRAM	Est. Speed	Fit Status
FP1616 bpw	70 GB	1.61 GB	74.81 GB (312%)	0 t/s	OOM
Q8_08.5 bpw	37.1 GB	1.61 GB	40.76 GB (170%)	0 t/s	OOM
Q6_K6.56 bpw	28.7 GB	1.61 GB	32.06 GB (134%)	~47 t/s	PARTIAL
Q5_K_M5.68 bpw	24.85 GB	1.61 GB	28.08 GB (117%)	~53 t/s	PARTIAL
Q4_K_M4.8 bpw	21 GB	1.61 GB	24.1 GB (100%)	~61 t/s	PARTIAL
FP44.25 bpw	18.55 GB	1.61 GB	21.56 GB (90%)	~305 t/s	FITS
IQ3_XS3.3 bpw	14.35 GB	1.61 GB	17.21 GB (72%)	~373 t/s	FITS

FAQ & Reference

Frequently Asked Questions: Qwen 3.6 35B-A3B MoE on NVIDIA GeForce RTX 3090 (24GB GDDR6X)

How much VRAM does Qwen 3.6 35B-A3B MoE need on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?

At standard 4-bit quantization (Q4_K_M) with 32K context, Qwen 3.6 35B-A3B MoE requires 24.1 GB of total VRAM (weights + KV cache + CUDA runtime overhead).

What inference speed will Qwen 3.6 35B-A3B MoE achieve on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?

Based on NVIDIA GeForce RTX 3090 (24GB GDDR6X)'s 936 GB/s memory bandwidth, expected decode speed is approximately 61 tokens per second.

Can I expand context beyond 32K tokens?

Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.