MacBook Pro M4 Max 96GBコスパ重視
96 GB Unified (+32)
C
This setup is broadly balanced for this model.68.3 tok/sデコード
〜$2,499 MSRP
Can glm 4 9b chat 1m run on NVIDIA A16 64GB?
YES — Runs Great
C46Usable
Estimated from fit model
glm 4 9b chat 1m needs ~14.1 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~85 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 14.1 GB, 85.2 tok/s, Runs well
14.1 GB required64.0 GB available
22% VRAM used
Fit status
Runs well
Decode
85.2 tok/s
TTFT
2271 ms
Safe context
772K
Memory
14.1 GB / 64.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsglm 4 9b chat 1m on NVIDIA A16 64GB
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: 85.2 tok/s decode · 2.3s TTFT (warm) · 213 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 85.2 tok/s | 1239 ms | 772K |
| Coding | C | Runs well | 85.2 tok/s | 2271 ms | 772K |
| Agentic Coding | C | Runs well | 85.2 tok/s | 3303 ms | 772K |
| Reasoning | C | Runs well | 85.2 tok/s | 2684 ms | 772K |
| RAG | C | Runs well | 85.2 tok/s | 4129 ms | 772K |
Quantization options
How glm 4 9b chat 1m (9B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C40 |
Q3_K_S | 3 | 4.4 GB | Low | C41 |
NVFP4 | 4 | 5.0 GB | Medium | C41 |
Q4_K_M | 4 | 5.5 GB | Medium | C41 |
Q5_K_M | 5 | 6.5 GB | High | C41 |
Q6_K | 6 | 7.4 GB | High | C41 |
Q8_0 | 8 | 9.6 GB | Very High | C41 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C43 |
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 startアップグレードオプション
glm 4 9b chat 1mを快適に動かすハードウェア
Frequently asked questions
Can NVIDIA A16 64GB run glm 4 9b chat 1m?
Yes, NVIDIA A16 64GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 85.2 tok/s.
How much VRAM does glm 4 9b chat 1m need?
glm 4 9b chat 1m (9B parameters) requires approximately 14.1 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 NVIDIA A16 64GB?
On NVIDIA A16 64GB, glm 4 9b chat 1m achieves approximately 85.2 tokens per second decode speed with a time-to-first-token of 2271ms using Q4_K_M quantization.
Can NVIDIA A16 64GB run glm 4 9b chat 1m for coding?
For coding workloads, glm 4 9b chat 1m on NVIDIA A16 64GB receives a C grade with 85.2 tok/s and 772K context.
What context window can glm 4 9b chat 1m use on NVIDIA A16 64GB?
On NVIDIA A16 64GB, glm 4 9b chat 1m can safely use up to 772K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--glm-4-9b-chat-1m-gguf-on-a16-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: