LLM Roofline
Qwen3 · Apache-2.0 · HF: Qwen/Qwen3-235B-A22B

Qwen3 235B-A22B — VRAM, KV Cache & Hardware Matrix

Serverless API Rate
$0.14 in / $0.80 out per 1M
Parameters
235.1B (22B act.)
Attention
GQA (4 KV / 64 Q)
Layers × Hidden
94 × 4096
Max Native Context
40k tokens
MoE Experts
8 / 128

Quantization Memory Footprint

Quantization BPW Weights Only Total @ 8k Ctx Total @ 32k Ctx Quality
FP16 16.00 446.67 GiB 449.25 GiB 453.65 GiB Lossless Reference
Q8_0 8.50 237.29 GiB 239.87 GiB 244.28 GiB Near-Lossless
Q6_K 6.57 183.41 GiB 185.99 GiB 190.40 GiB High Precision
Q5_K_M 5.69 158.85 GiB 161.43 GiB 165.83 GiB Balanced High
Q4_K_M 4.85 135.40 GiB 137.98 GiB 142.38 GiB Recommended 4-bit
MLX_4BIT 4.50 125.62 GiB 128.21 GiB 132.61 GiB Apple Silicon Optimized
IQ4_XS 4.25 118.65 GiB 121.23 GiB 125.63 GiB Compact 4-bit
Q3_K_M 3.91 109.15 GiB 111.74 GiB 116.14 GiB Aggressive Compression

Hardware Compatibility & Speed Leaderboard (Q4_K_M @ 32k Context)

Hardware Status Total / Limit Decode TTFT (4k) Max Safe Ctx Inspect
Apple Mac Studio M3 Ultra (256 GB) COMFORTABLE 142.4 / 199.7 GiB 28.2 tok/s 6.16s 40k Simulate →
Apple Mac Studio M3 Ultra (512 GB) COMFORTABLE 142.4 / 399.4 GiB 28.2 tok/s 6.16s 40k Simulate →
Apple Mac Studio M2 Ultra (192 GB) TIGHT 142.4 / 149.8 GiB 27.6 tok/s 6.80s 40k Simulate →
Cloud NVIDIA H200 SXM (141 GB) OOM 142.6 / 140.4 GiB OOM — 20k Simulate →
Cloud NVIDIA H100 SXM (80 GB) OOM 142.6 / 79.4 GiB OOM — OOM Simulate →
Cloud NVIDIA A100 SXM (80 GB) OOM 142.6 / 79.4 GiB OOM — OOM Simulate →
NVIDIA RTX PRO 6000 Blackwell (96 GB) OOM 142.6 / 95.0 GiB OOM — OOM Simulate →
NVIDIA RTX 5090 (32 GB) OOM 142.6 / 31.0 GiB OOM — OOM Simulate →
NVIDIA RTX 4090 (24 GB) OOM 142.6 / 23.0 GiB OOM — OOM Simulate →
NVIDIA RTX 5080 (16 GB) OOM 142.6 / 15.0 GiB OOM — OOM Simulate →
NVIDIA RTX 3090 (24 GB Used) OOM 142.6 / 23.0 GiB OOM — OOM Simulate →
NVIDIA RTX 5070 Ti (16 GB) OOM 142.6 / 15.0 GiB OOM — OOM Simulate →
AMD Radeon RX 7900 XTX (24 GB) OOM 142.6 / 23.0 GiB OOM — OOM Simulate →
Cloud NVIDIA L40S (48 GB) OOM 142.6 / 47.4 GiB OOM — OOM Simulate →
2x NVIDIA RTX 3090 Rig (48 GB) OOM 142.6 / 46.6 GiB OOM — OOM Simulate →
Apple Mac Studio M4 Max (64 GB) OOM 142.4 / 49.9 GiB OOM — OOM Simulate →
Apple Mac Studio M4 Max (128 GB) OOM 142.4 / 99.8 GiB OOM — OOM Simulate →
NVIDIA RTX 5060 Ti (16 GB) OOM 142.6 / 15.0 GiB OOM — OOM Simulate →
Apple MacBook Pro M3 Max (64 GB) OOM 142.4 / 49.9 GiB OOM — OOM Simulate →
Apple MacBook Pro M3 Max (128 GB) OOM 142.4 / 99.8 GiB OOM — OOM Simulate →
NVIDIA RTX 3060 (12 GB) OOM 142.6 / 11.0 GiB OOM — OOM Simulate →
Apple Mac Mini M4 Pro (48 GB) OOM 142.4 / 37.4 GiB OOM — OOM Simulate →
Apple Mac Mini M4 Pro (64 GB) OOM 142.4 / 49.9 GiB OOM — OOM Simulate →
Ryzen AI Max+ 395 Strix Halo (128 GB) OOM 142.4 / 96.0 GiB OOM — OOM Simulate →
Ryzen AI Max+ 395 Strix Halo (64 GB) OOM 142.4 / 48.0 GiB OOM — OOM Simulate →
Apple Mac Mini M4 (16 GB) OOM 142.4 / 10.0 GiB OOM — OOM Simulate →
Apple Mac Mini M4 (24 GB) OOM 142.4 / 18.0 GiB OOM — OOM Simulate →
Ryzen AI 9 HX 370 Mini-PC (64 GB) OOM 142.4 / 48.0 GiB OOM — OOM Simulate →

Interactive Qwen3 235B-A22B Simulator

235.1B (22B active) · GQA
819 GB/s · 58 TFLOPS
3. Weight Quantization 4.85 bpw
4. KV Cache
32,768 tok
Compute Roofline
COMFORTABLE FIT Memory-Bandwidth Bound
Max Safe Ctx: 40k
Memory Allocation Footprint 142.38 / 199.68 GiB usable
Weights: 135.40 GiB KV Cache: 5.88 GiB Overhead: 1.11 GiB Headroom: 57.30 GiB
Decode Speed
28.2
tok/s (batch=1)
Prefill Speed
669
prompt tok/s
TTFT Latency
6.16s
for 4,096 tok
Active Params
22.0B
of 235.1B total
llama.cpp Server Command
llama-server -hf Qwen/Qwen3-235B-A22B:Q4_K_M -ngl 99 -fa on -c 32768 --port 8080
Context KV Cache Total VRAM Decode Status
4k 0.73 GiB 136.96 GiB 39.1 tok/s COMFORTABLE
8k 1.47 GiB 137.98 GiB 37.0 tok/s COMFORTABLE
16k 2.94 GiB 139.45 GiB 33.5 tok/s COMFORTABLE
32k 5.88 GiB 142.38 GiB 28.2 tok/s COMFORTABLE

Buy-vs-Cloud-vs-API Monthly TCO Break-Even

USE SERVERLESS API
Own Hardware/mo
$109.58
168.4h active + power
Cloud GPU/mo
$675.74
@$3.49/hr spot/pod
Serverless API/mo
$19.00
Payback: 346.2 mo

Engineering FAQ — Qwen3 235B-A22B

How much VRAM does Qwen3 235B-A22B require in Q4_K_M?

Qwen3 235B-A22B weights occupy 135.40 GiB in Q4_K_M. Including 32k FP16 KV cache and runtime buffers, total memory is 142.38 GiB.

What is the cheapest local hardware that runs Qwen3 235B-A22B?

Apple Mac Studio M3 Ultra (256 GB) (256 GB) runs Qwen3 235B-A22B Q4_K_M at ~28.2 tok/s decode and 6.16s TTFT.

How does Qwen3 235B-A22B scale KV cache at long context?

Qwen3 235B-A22B uses GQA with 4 KV heads across 94 layers (128 head_dim).