Yi 1.5 34B needs ~27.7 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~7 tok/s.
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
3.7 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~2.8 GB host RAM)
Decode
7.4 tok/s
TTFT
26317 ms
Safe context
4K
Memory
27.7 GB / 24.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~1.5 GB host RAM) | 8.5 tok/s | 12486 ms | 4K |
| Coding | C | Very compromised (needs ~2.8 GB host RAM) | 7.4 tok/s | 26317 ms | 4K |
| Agentic Coding | F | Too heavy | 5.7 tok/s | 49314 ms | 4K |
| Reasoning | C | Very compromised (needs ~2.8 GB host RAM) | 7.4 tok/s | 31102 ms | 4K |
| RAG | F | Too heavy | 5.7 tok/s | 61642 ms | 4K |
How Yi 1.5 34B (34B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.3 GB | Low | B63 |
Q3_K_SBest for your GPU | 3 | 16.7 GB | Low | B62 |
NVFP4 | 4 | 19.0 GB | Medium | F0 |
Q4_K_M | 4 | 20.7 GB | Medium | F0 |
Q5_K_M | 5 | 24.5 GB | High | F0 |
Q6_K | 6 | 27.9 GB | High | F0 |
Q8_0 | 8 | 36.4 GB | Very High | F0 |
F16 | 16 | 69.7 GB | Maximum | F0 |
Copy-paste commands to run Yi 1.5 34B on your machine.
Run
lms load Yi-1.5-34B-Chat && lms server startアップグレードオプション
NVIDIA A100 40GB最大の飛躍Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 824%.
〜$10,000 MSRP
Intel Data Center GPU Max 1550 128GBコスパ重視Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 1326%.
〜$15,000 MSRP
Gaudi 3 128GBコスパ最良Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 1732%.
〜$15,000 MSRP
Yes, Intel Arc Pro B60 24GB can run Yi 1.5 34B with a C grade (Very compromised (needs ~2.8 GB host RAM)). Expected decode speed: 7.4 tok/s.
Yi 1.5 34B (34B parameters) requires approximately 27.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 34B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B60 24GB, Yi 1.5 34B achieves approximately 7.4 tokens per second decode speed with a time-to-first-token of 26317ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 34B on Intel Arc Pro B60 24GB receives a C grade with 7.4 tok/s and 4K context.
On Intel Arc Pro B60 24GB, Yi 1.5 34B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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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