Baichuan 13B needs ~23.3 GB but Intel Arc A750 8GB only has 8.0 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
15.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.6 tok/s
TTFT
53796 ms
Safe context
4K
Memory
23.3 GB / 8.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 23.3 GB, but this setup only exposes 8.0 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 | 3.6 tok/s | 29267 ms | 4K |
| Coding | F | Too heavy | 3.6 tok/s | 53796 ms | 4K |
| Agentic Coding | F | Too heavy | 3.6 tok/s | 78249 ms | 4K |
| Reasoning | F | Too heavy | 3.6 tok/s | 63577 ms | 4K |
| RAG | F | Too heavy | 3.6 tok/s | 97811 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Baichuan 13B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 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? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q5_K_M | 130.8 | Fits |
| RTX 4090 24GB | 24 GB | Q5_K_M | 61.0 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q5_K_M | 60.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q5_K_M | 52.4 | Offloads |
Mac Studio M2 Ultra 128GB | 128 GB | Q5_K_M | 50.6 | Fits |
| RTX 3090 24GB | 24 GB | Q5_K_M | 48.7 | Offloads |
Mac Studio M1 Ultra 128GB | 128 GB | Q5_K_M | 47.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q5_K_M | 33.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q5_K_M | 33.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q5_K_M | 26.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q5_K_M | 24.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q5_K_M | 20.6 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q5_K_M | 20.2 | Fits |
| RTX 4070 12GB | 12 GB | Q5_K_M | 7.2 | Too big |
| RTX 3060 12GB | 12 GB | Q5_K_M | 4.5 | Too big |
| RTX 4060 8GB | 8 GB | Q5_K_M | 3.2 | Too big |
Estimates for single-stream decoding at Q5_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.
How Baichuan 13B (13B params) fits at each quantization level on Intel Arc A750 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 5.1 GB | Low | B69 |
Q3_K_S | 3 | 6.4 GB | Low | F0 |
NVFP4 | 4 | 7.3 GB | Medium | F0 |
Q4_K_M | 4 | 7.9 GB | Medium | F0 |
Q5_K_M | 5 | 9.4 GB | High | F0 |
Q6_K | 6 | 10.7 GB | High | F0 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Upgrade options
Intel Arc Pro B60 24GBBudget pickMakes 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.
~$599 MSRP
RTX 5090 32GBBiggest leapMakes 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.
~$1,999 MSRP
Intel Data Center GPU Max 1550 128GBBest valueMakes 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.
~$15,000 MSRP
Gaudi 3 128GBIntel upgradeMakes 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.
~$15,000 MSRP
No, Baichuan 13B requires more memory than Intel Arc A750 8GB provides.
Baichuan 13B (13B parameters) requires approximately 23.3 GB of memory with Q5_K_M quantization.
The recommended quantization for Baichuan 13B is Q5_K_M, which balances quality and memory efficiency.
On Intel Arc A750 8GB, Baichuan 13B achieves approximately 3.6 tokens per second decode speed with a time-to-first-token of 53796ms using Q5_K_M quantization.
For coding workloads, Baichuan 13B on Intel Arc A750 8GB receives a F grade with 3.6 tok/s and 4K context.
On Intel Arc A750 8GB, Baichuan 13B can safely use up to 4K tokens of context. The model's official context limit is 8K, 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.
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.
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