Intel Arc B580 12GBGünstige Wahl12 GB VRAM (+2)456 GB/s (+76)
C
The raw memory story may look fine, but the software ecosystem is still a constraint here.39.9 tok/s Dekodierung
ca. $249 MSRP
Can glm 4 9b chat 1m run on Intel Arc B570 10GB?
YES — Tight Fit
C51Usable
Estimated from fit model
glm 4 9b chat 1m needs ~8.4 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~37 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 8.4 GB, 37.4 tok/s, Tight fit
8.4 GB required10.0 GB available
84% VRAM used
Fit status
Tight fit
Decode
37.4 tok/s
TTFT
5180 ms
Safe context
40K
Memory
8.4 GB / 10.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsglm 4 9b chat 1m on Intel Arc B570 10GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 37.4 tok/s decode · 5.2s TTFT (warm) · 93 tok/s prefill
What limits this setup
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.
Best improvement path
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.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 37.4 tok/s | 2825 ms | 40K |
| Coding | C | Tight fit | 37.4 tok/s | 5180 ms | 40K |
| Agentic Coding | C | Tight fit | 37.4 tok/s | 7534 ms | 40K |
| Reasoning | C | Tight fit | 37.4 tok/s | 6121 ms | 40K |
| RAG | C | Tight fit | 37.4 tok/s | 9418 ms | 40K |
Quantization options
How glm 4 9b chat 1m (9B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C52 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4 | 4 | 5.0 GB | Medium | C53 |
Q4_K_M | 4 | 5.5 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 6.5 GB | High | C52 |
Q6_K | 6 | 7.4 GB | High | F0 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
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 startUpgrade-Optionen
Hardware, die glm 4 9b chat 1m gut ausführt
Intel Arc B580 12GBGünstige WahlIntel Arc A770 16GBBestes Preis-Leistungs-Verhältnis16 GB VRAM (+6)560 GB/s (+180)
C
Adds memory headroom for longer context windows and future model growth.45.9 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $349 MSRP
Intel Arc Pro A60 12GBIntel-Upgrade12 GB VRAM (+2)384 GB/s (+4)
C
The raw memory story may look fine, but the software ecosystem is still a constraint here.34.3 tok/s Dekodierung
ca. $499 MSRP
Frequently asked questions
Can Intel Arc B570 10GB run glm 4 9b chat 1m?
Yes, Intel Arc B570 10GB can run glm 4 9b chat 1m with a C grade (Tight fit). Expected decode speed: 37.4 tok/s.
How much VRAM does glm 4 9b chat 1m need?
glm 4 9b chat 1m (9B parameters) requires approximately 8.4 GB of memory with Q4_K_M quantization.
What is the best quantization for glm 4 9b chat 1m?
The recommended quantization for glm 4 9b chat 1m is Q4_K_M, which balances quality and memory efficiency.
What speed will glm 4 9b chat 1m run at on Intel Arc B570 10GB?
On Intel Arc B570 10GB, glm 4 9b chat 1m achieves approximately 37.4 tokens per second decode speed with a time-to-first-token of 5180ms using Q4_K_M quantization.
Can Intel Arc B570 10GB run glm 4 9b chat 1m for coding?
For coding workloads, glm 4 9b chat 1m on Intel Arc B570 10GB receives a C grade with 37.4 tok/s and 40K context.
What context window can glm 4 9b chat 1m use on Intel Arc B570 10GB?
On Intel Arc B570 10GB, glm 4 9b chat 1m can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if glm 4 9b chat 1m feels slow on Intel Arc B570 10GB?
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.
Would CUDA be a better path than Intel Arc B570 10GB for glm 4 9b chat 1m?
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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