willitrun·ai

Can glm 4 9b chat 1m run on RTX 3090 24GB?

YES — Runs Great

C51Usable
Estimated from fit model

glm 4 9b chat 1m needs ~10.1 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~119 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) — 10.1 GB, 119.3 tok/s, Runs well
10.1 GB required24.0 GB available
42% VRAM used

Fit status

Runs well

Decode

119.3 tok/s

TTFT

1622 ms

Safe context

226K

Memory

10.1 GB / 24.0 GB

Memory breakdown

Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsglm 4 9b chat 1m on RTX 3090 24GB
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: 119.3 tok/s decode · 1.6s TTFT (warm) · 298 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 well119.3 tok/s885 ms226K
CodingCRuns well119.3 tok/s1622 ms226K
Agentic CodingCRuns well119.3 tok/s2360 ms226K
ReasoningCRuns well119.3 tok/s1917 ms226K
RAGCRuns well119.3 tok/s2949 ms226K

Inference speed

glm 4 9b chat 1m inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for glm 4 9b chat 1m at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M126.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M126.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M125.9Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M119.3Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M111.3Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M101.4Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M84.5Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M80.1Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M68.9Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M68.3Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M68.3Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M43.7Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M43.3Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M40.1Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M35.2Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M23.4Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How glm 4 9b chat 1m (9B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowC45
Q3_K_S
3
4.4 GB
LowC45
NVFP4
4
5.0 GB
MediumC45
Q4_K_M
4
5.5 GB
MediumC46
Q5_K_M
5
6.5 GB
HighC46
Q6_K
6
7.4 GB
HighC47
Q8_0
8
9.6 GB
Very HighC48
F16Best for your GPU
16
18.5 GB
MaximumC49

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

Frequently asked questions

Can RTX 3090 24GB run glm 4 9b chat 1m?

Yes, RTX 3090 24GB can run glm 4 9b chat 1m with a C grade (Runs well). Expected decode speed: 119.3 tok/s.

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

glm 4 9b chat 1m (9B parameters) requires approximately 10.1 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 RTX 3090 24GB?

On RTX 3090 24GB, glm 4 9b chat 1m achieves approximately 119.3 tokens per second decode speed with a time-to-first-token of 1622ms using Q4_K_M quantization.

Can RTX 3090 24GB run glm 4 9b chat 1m for coding?

For coding workloads, glm 4 9b chat 1m on RTX 3090 24GB receives a C grade with 119.3 tok/s and 226K context.

What context window can glm 4 9b chat 1m use on RTX 3090 24GB?

On RTX 3090 24GB, glm 4 9b chat 1m can safely use up to 226K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3090 24GBSee all hardware for glm 4 9b chat 1m
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<iframe src="https://willitrunai.com/embed/hf-bartowski--glm-4-9b-chat-1m-gguf-on-rtx-3090-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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