Hy3 needs ~186.1 GB but Intel Arc A370M 4GB only has 4.0 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
182.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
4K
Memory
186.1 GB / 4.0 GB
Offload
100%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 186.1 GB, but this setup only exposes 4.0 GB of usable VRAM.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.0 tok/s | 52800 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 96800 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 114400 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 176000 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Hy3 at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~12 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 11.6 | Heavy offload |
How Hy3 (295B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q1_0_G128 | 1.125 | 42.5 GB | Very Low | F0 |
Q2_0_G128 | 1.71 | 78.8 GB | Low | F0 |
Q2_K | 2 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 480%.
~$6,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$8,000 MSRP
No, Hy3 requires more memory than Intel Arc A370M 4GB provides.
Hy3 (295B parameters) requires approximately 186.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Hy3 is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A370M 4GB, Hy3 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.
For coding workloads, Hy3 on Intel Arc A370M 4GB receives a F grade with 2.0 tok/s and 4K context.
On Intel Arc A370M 4GB, Hy3 can safely use up to 4K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hy3-on-arc-a370m-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 128 GB |
| Q4_K_M |
| 5.5 |
| Too big |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 5.2 | Too big |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 4.1 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 4.1 | Too big |
 RTX 5090 32GB | 32 GB | Q4_K_M | 3.0 | Too big |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 3.0 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 2.8 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 2.6 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.5 | Too big |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 2.1 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 2.0 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
| Low |
| F0 |
Q3_K_S | 3 | 144.6 GB | Low | F0 |
NVFP4 | 4 | 165.2 GB | Medium | F0 |
Q4_K_M | 4 | 180.0 GB | Medium | F0 |
Q5_K_M | 5 | 212.4 GB | High | F0 |
Q6_K | 6 | 241.9 GB | High | F0 |
Q8_0 | 8 | 315.7 GB | Very High | F0 |
F16 | 16 | 604.8 GB | Maximum | F0 |
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
