Will It Run AI

Can glm 4 9b chat 1m run on NVIDIA L40S 48GB?

YES — Runs Great

C47Usable
Estimated from fit model

glm 4 9b chat 1m needs ~12.5 GB VRAM. NVIDIA L40S 48GB has 48.0 GB. With Q4_K_M quantization, expect ~123 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.5 GB, 122.8 tok/s, Runs well
12.5 GB required48.0 GB available
26% VRAM used

Fit status

Runs well

Decode

122.8 tok/s

TTFT

1577 ms

Safe context

554K

Memory

12.5 GB / 48.0 GB

Memory breakdown

Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsglm 4 9b chat 1m on NVIDIA L40S 48GB
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: 122.8 tok/s decode · 1.6s TTFT (warm) · 307 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well122.8 tok/s860 ms554K
CodingCRuns well122.8 tok/s1577 ms554K
Agentic CodingCRuns well122.8 tok/s2294 ms554K
ReasoningCRuns well122.8 tok/s1864 ms554K
RAGCRuns well122.8 tok/s2868 ms554K

Quantization options

How glm 4 9b chat 1m (9B params) fits at each quantization level on NVIDIA L40S 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowC41
Q3_K_S
3
4.4 GB
LowC42
NVFP4
4
5.0 GB
MediumC42
Q4_K_M
4
5.5 GB
MediumC42
Q5_K_M
5
6.5 GB
HighC42
Q6_K
6
7.4 GB
HighC42
Q8_0
8
9.6 GB
Very HighC43
F16Best for your GPU
16
18.5 GB
MaximumC45

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

Opciones de mejora

Hardware que ejecuta bien glm 4 9b chat 1m

Mac Studio M3 Ultra 96GBOpción económica
96 GB Unified (+48)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.101.4 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Frequently asked questions

Can NVIDIA L40S 48GB run glm 4 9b chat 1m?

Yes, NVIDIA L40S 48GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 122.8 tok/s.

How much VRAM does glm 4 9b chat 1m need?

glm 4 9b chat 1m (9B parameters) requires approximately 12.5 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 L40S 48GB?

On NVIDIA L40S 48GB, glm 4 9b chat 1m achieves approximately 122.8 tokens per second decode speed with a time-to-first-token of 1577ms using Q4_K_M quantization.

Can NVIDIA L40S 48GB run glm 4 9b chat 1m for coding?

For coding workloads, glm 4 9b chat 1m on NVIDIA L40S 48GB receives a C grade with 122.8 tok/s and 554K context.

What context window can glm 4 9b chat 1m use on NVIDIA L40S 48GB?

On NVIDIA L40S 48GB, glm 4 9b chat 1m can safely use up to 554K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L40S 48GBSee all hardware for glm 4 9b chat 1m
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