glm 4 9b chat 1m needs ~8.6 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~34 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
Fit status
Runs well
Decode
34.3 tok/s
TTFT
5649 ms
Safe context
67K
Memory
8.6 GB / 12.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
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.
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 | C | Runs well | 34.3 tok/s | 3081 ms | 67K |
| Coding | C | Runs well | 34.3 tok/s | 5649 ms | 67K |
| Agentic Coding | C | Runs well | 34.3 tok/s | 8216 ms | 67K |
| Reasoning | C | Runs well | 34.3 tok/s | 6676 ms | 67K |
| RAG | C | Runs well | 34.3 tok/s | 10270 ms | 67K |
How glm 4 9b chat 1m (9B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C50 |
Q3_K_S | 3 | 4.4 GB | Low | C51 |
NVFP4 | 4 | 5.0 GB | Medium | C52 |
Q4_K_M | 4 | 5.5 GB | Medium | C53 |
Q5_K_M | 5 | 6.5 GB | High | C52 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | C52 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Copy-paste commands to run glm 4 9b chat 1m on your machine.
Run
lms load hf-bartowski--glm-4-9b-chat-1m-gguf && lms server startアップグレードオプション
RTX 5070 Ti 16GBコスパ重視Raises estimated decode speed by about 205%.
Adds memory headroom for longer context windows and future model growth.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
〜$749 MSRP
RTX 4070 Ti Super 16GBコスパ最良Raises estimated decode speed by about 163%.
Adds memory headroom for longer context windows and future model growth.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
〜$799 MSRP
Yes, Intel Arc Pro A60 12GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 34.3 tok/s.
glm 4 9b chat 1m (9B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.
The recommended quantization for glm 4 9b chat 1m is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro A60 12GB, glm 4 9b chat 1m achieves approximately 34.3 tokens per second decode speed with a time-to-first-token of 5649ms using Q4_K_M quantization.
For coding workloads, glm 4 9b chat 1m on Intel Arc Pro A60 12GB receives a C grade with 34.3 tok/s and 67K context.
On Intel Arc Pro A60 12GB, glm 4 9b chat 1m can safely use up to 67K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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.
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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