Qwen3 235B A22B Instruct 2507 — Hardware Requirements & GPU Compatibility
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Qwen3 235B A22B Instruct 2507 is Alibaba's flagship instruction-tuned model from the July 2025 update, featuring 235 billion total parameters with approximately 22 billion active during inference. As the largest instruct model in the Qwen3 lineup, it delivers top-tier conversational quality, knowledge depth, and instruction following. Despite its massive total parameter count, the MoE architecture keeps active compute manageable. Running this model locally still requires substantial hardware, typically multi-GPU setups with 48 GB or more of total VRAM, but the 2507 refresh makes it one of the most capable open-weight models available for users with high-end local infrastructure.
Qwen3 235B A22B Instruct 2507 runs on a 192 GB+ Mac or a datacenter GPU
Best local pick → Mac Pro M2 Ultra (192 GB) · Q4_K_M · 142.9 GB · ~23 tok/s
Macs from 96 GB (IQ2_M · ~31 tok/s)
Score 98.5#63 of 106 open#41 of 82 that fit ≤192 GBApache 2.0
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
llmrun Composite Score →
98.5
- 90% range 95.0–101.9
- #63 of 106 open models
- #41 of 82 that fit ≤192 GB
Run Qwen3 235B A22B Instruct 2507
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run qwen3:235b-instructQ4_K_M runs on one GH200 Grace Hopper Superchip 144 GB · 16K context
Download: Q4_K_M/Qwen3-235B-A22B-Instruct-2507-Q4_K_M-00001-of-00003.gguf142.15 GB + 2 partsunsloth
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on B200 (192 GB) (49 GB headroom) · Q4_K_M
- Generation speed
- ~129 tok/s
- generation speed
- Cost per 1M output tokens
- $12.86
- per 1M output tokens
How Much VRAM Does Qwen3 235B A22B Instruct 2507 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | Runs on | |
|---|---|---|---|---|
| 100.4 GB | 103.4 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Runs on Instinct MI250X 128 GB | |
| 103.3 GB | 106.3 GB | 3-bit small — 3-bit small quantization | Runs on Instinct MI250X 128 GB | |
| 115.1 GB | 118.1 GB | 3-bit medium — 3-bit medium quantization | Runs on Instinct MI250X 128 GB | |
| 118.0 GB | 121 GB | 4-bit · legacy — 4-bit legacy quantization | Runs on Instinct MI250X 128 GB | |
| 141.6 GB | 144.5 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Runs on GH200 Grace Hopper Superchip 144 GB | |
| 168 GB | 171 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Runs on B200 192 GB | |
| 194.4 GB | 197.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Runs on Instinct MI325X 256 GB | |
| 235.6 GB | 238.6 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Runs on Instinct MI325X 256 GB |
Selected Q4_K_M
4-bit medium quantization142.9GBat 16K context
- Weights
- 141.1 GB
- KV cache
- 1.6 GB
- Overhead
- 0.3 GB
Server-class: ~142.9 GB at 16K context · one GH200 Grace Hopper Superchip 144 GB
Or .
Which Hardware Can Run Qwen3 235B A22B Instruct 2507?
Q4_K_M · 141.6 GBQwen3 235B A22B Instruct 2507 (Q4_K_M) requires 141.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 185+ GB is recommended. Using the full 262K context window can add up to 25.0 GB, bringing total usage to 166.6 GB. No consumer GPU has enough memory. 4 devices with unified memory can run Qwen3 235B A22B Instruct 2507, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (192 GB).
128–144 GB
Q4_K_M · best fit Q3_K_L (122.4 GB)
4 GPUs · 2 Mac classes
GH200 Grace Hopper Superchip~114Simulate output on GH200 Grace Hopper SuperchipH200 NVL~113Simulate output on H200 NVLH200 SXM~113Simulate output on H200 SXMM2 Ultra*128 GB~26Simulate output on Mac Pro M2 Ultra (128 GB)M5 Max128 GB~22Simulate output on Mac Studio M5 Max (128 GB)192+ GB
fits · 49.1 GB free · up to 256K ctx
6 GPUs · 1 Mac class · 2 device families
B200~129Simulate output on B200B300~129Simulate output on B300Instinct MI350X~46Simulate output on Instinct MI350XM3 Ultra*256–512 GB~24Simulate output on Mac Studio (M3 Ultra, 256GB)8x H100 SXM5~168Simulate output on NVIDIA DGX H1008x A100 SXM4~156Simulate output on NVIDIA DGX A100 640GB
By VRAM tier · typical GPUs and Macs at Q4_K_M
Consumer GPUs (up to 32 GB) and Macs up to 64 GB: too small for this model (needs ≥ 72.4 GB)8 GB tier →12 GB tier →16 GB tier →24 GB tier →Macs →
How good is Qwen3 235B A22B Instruct 2507?
The llmrun Score merges 8 public benchmark boards into a single figure, where 100 represents the average for open models.
Score vs VRAM in the ≤192 GB tier
- Qwen3 235B A22B Instruct 2507
- Other models
- Best Score for its VRAM
#41 of 82 that fit ≤192 GB
Each dot is a model at the best quant that fits ≤192 GB. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.
Full ≤192 GB ranking →Benchmarks
The 6 boards that weigh most in the llmrun Score, of 9 with a result for this model; badges show who ran the test.
- LMArena Text
LMArena · Oct 2026Arena
Arena1422.2
#33/188 open
- ARC-AGI-2
Epoch AI · Jul 2025Independent
Independent1.3%
#18/22 open
- MMLU-Pro
TIGER-LabIndependent
Independent83.0%
#14/146 open
- Aider Polyglot
Epoch AI · Jul 2025Independent
Independent59.6%
#4/18 open
- ARC-AGI
Epoch AI · Jul 2025Independent
Independent11.0%
#19/22 open
- WeirdML
Epoch AI · Jul 2025Independent
Independent38.7%
#27/47 open
In the ≤192 GB class
82 models fit ≤192 GBSame Score, only models that fit ≤192 GB at their best quant: the ranking a ≤192 GB owner actually chooses from.
- #1
MiMo V2.6 Flash RLXiaomi
131.9
~229 tok/s · 186.5 GB
- #2
Step 5 PreviewStepFun
128.0
~151 tok/s · 191.4 GB
- #3
DeepSeek V4 Flash 0731DeepSeek
127.6
~181 tok/s · 183.0 GB
- #4
GLM 5.3 FlashZ.ai
124.1
~228 tok/s · 185.1 GB
- #5
Qwen3.8 27BAlibaba
122.0
~299 tok/s · 17.9 GB
- #6
Inkling SmallThinking Machines
119.6
~399 tok/s · 162.7 GB
Alternatives to Qwen3 235B A22B Instruct 2507
Qwen 3 family · 79 models →Official models with an llmrun Score, each answering one question; sizes are each model's default quant (usually Q4_K_M) with a 16K-token context.
Best alternative at this size
Inkling Small
Thinking Machines
- 21.1 higher Score
- 19.8 GB more VRAM
Score 119.6 · 162.7 GB
Other vendor
MiMo V2.6 Flash RL
Xiaomi
- 33.4 higher Score
- 43.6 GB more VRAM
Score 131.9 · 186.5 GB
Similar models
10| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| DeepSeek V4 Flash 0731DeepSeek | 127.6 | 183 GB | —needs > 24 GB | similar score |
| GLM 5.3 FlashZ.ai | 124.1 | 205.2 GB | —needs > 24 GB | similar score |
| DeepSeek V4 FlashDeepSeek | 115.3 | 175.1 GB | —needs > 24 GB | similar score |
| Qwen3.5 397B A17BAlibaba | 112.8 | 242.6 GB | —needs > 24 GB | similar score |
| MiniMax M3MiniMax | 112.6 | 258 GB | —needs > 24 GB | similar score |
| MiniMax M2.5MiniMax | 111.8 | 139.6 GB | —needs > 24 GB | similar score |
| Qwen3.5 122B A10BAlibaba | 109.4 | 75.5 GB | —needs > 24 GB | similar score |
| GLM 4.7Z.ai | 109.1 | 217.9 GB | —needs > 24 GB | similar score |
| MiMo V2.5Xiaomi | 108.3 | 187.6 GB | —needs > 24 GB | similar score |
| GLM 4.6Z.ai | 103.1 | 216.9 GB | —needs > 24 GB | similar score |
Fine-tunes & variants(2)
Specifications
- Publisher
- Alibaba
- Family
- Qwen 3
- Parameters
- 235.1B
- Active parameters
- 22.2B per token
- Architecture
- Qwen3MoeForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-07-21
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Qwen3 235B A22B Instruct 2507 need?
Qwen3 235B A22B Instruct 2507 requires 141.6 GB of VRAM at Q4_K_M, or 470.7 GB at BF16. Full 262K context adds up to 25.0 GB (166.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 235.1B × 4.8 bits ÷ 8 = 141.1 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 25.5 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run Qwen3 235B A22B Instruct 2507?
No — Qwen3 235B A22B Instruct 2507 requires at least 71.0 GB at IQ2_XS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for Qwen3 235B A22B Instruct 2507?
For Qwen3 235B A22B Instruct 2507, Q4_K_M (141.6 GB) offers the best balance of quality and VRAM usage. Q4_K_L (144.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XS at 71.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen3 235B A22B Instruct 2507 on a Mac?
Yes — Mac Studio (M3 Ultra, 96GB) and 20 other Macs can run Qwen3 235B A22B Instruct 2507, the smallest at IQ2_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 141.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Pro M2 Ultra (192 GB).
Can I run Qwen3 235B A22B Instruct 2507 locally?
Yes — Qwen3 235B A22B Instruct 2507 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwen3 235B A22B Instruct 2507?
At Q4_K_M, Qwen3 235B A22B Instruct 2507 can reach ~46 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 13.4 GB active ÷ (8000 × 0.65) = 2.57 ms, plus 94 layers × 0.055 ms = 5.17 ms, so 1000 ÷ 7.74 ms = ~129 tok/s
Estimated speed at Q4_K_M (141.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Qwen3 235B A22B Instruct 2507?
At Q4_K_M, the download is about 141.06 GB. The full-precision BF16 version is 470.19 GB. The smallest option (IQ2_XS) is 70.53 GB.
Which GPUs can run Qwen3 235B A22B Instruct 2507?
No single consumer GPU has enough VRAM to run Qwen3 235B A22B Instruct 2507 at Q4_K_M (141.6 GB). Multi-GPU or professional hardware is required.
Which devices can run Qwen3 235B A22B Instruct 2507?
4 devices with unified memory can run Qwen3 235B A22B Instruct 2507 at Q4_K_M (141.6 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.