willitrun·ai

Can glm 4 9b chat 1m run on Radeon Pro W7500 8GB?

YES — With Offload

C48Usable
Estimated from fit model

glm 4 9b chat 1m needs ~8.2 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With Q4_K_M quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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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) — 8.2 GB, 16.9 tok/s, Runs with offload (needs ~0.2 GB host RAM)
8.2 GB required8.0 GB available
102% VRAM needed

0.2 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

16.9 tok/s

TTFT

11425 ms

Safe context

12K

Memory

8.2 GB / 8.0 GB

Memory breakdown

Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsglm 4 9b chat 1m on Radeon Pro W7500 8GB
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: 16.9 tok/s decode · 11.4s TTFT (warm) · 42 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload24.1 tok/s4387 ms12K
CodingCRuns with offload (needs ~0.2 GB host RAM)16.9 tok/s11425 ms12K
Agentic CodingDVery compromised (needs ~0.8 GB host RAM)13.2 tok/s21411 ms12K
ReasoningCRuns with offload (needs ~0.2 GB host RAM)16.9 tok/s13502 ms12K
RAGDVery compromised (needs ~0.8 GB host RAM)13.2 tok/s26764 ms12K

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 Radeon Pro W7500 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowC54
Q3_K_S
3
4.4 GB
LowC53
NVFP4Best for your GPU
4
5.0 GB
MediumC53
Q4_K_M
4
5.5 GB
MediumF0
Q5_K_M
5
6.5 GB
HighF0
Q6_K
6
7.4 GB
HighF0
Q8_0
8
9.6 GB
Very HighF0
F16
16
18.5 GB
MaximumF0

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

Opções de upgrade

Hardware que roda bem glm 4 9b chat 1m

RX 7600 XT 16GBOpção econômica
16 GB VRAM (+8)288 GB/s (+64)
C
Raises estimated decode speed by about 80%.30.4 tok/s decodificação

Raises estimated decode speed by about 80%.

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RX 9060 XT 16GBMelhor custo-benefício
16 GB VRAM (+8)320 GB/s (+96)
C
Raises estimated decode speed by about 117%.36.7 tok/s decodificação

Raises estimated decode speed by about 117%.

Adds memory headroom for longer context windows and future model growth.

~$349 MSRP

RX 7700 XT 12GBUpgrade AMD
12 GB VRAM (+4)432 GB/s (+208)
C
Raises estimated decode speed by about 179%.47.2 tok/s decodificação

Raises estimated decode speed by about 179%.

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

Frequently asked questions

Can Radeon Pro W7500 8GB run glm 4 9b chat 1m?

Yes, Radeon Pro W7500 8GB can run glm 4 9b chat 1m with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 16.9 tok/s.

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

glm 4 9b chat 1m (9B parameters) requires approximately 8.2 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 Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, glm 4 9b chat 1m achieves approximately 16.9 tokens per second decode speed with a time-to-first-token of 11425ms using Q4_K_M quantization.

Can Radeon Pro W7500 8GB run glm 4 9b chat 1m for coding?

For coding workloads, glm 4 9b chat 1m on Radeon Pro W7500 8GB receives a C grade with 16.9 tok/s and 12K context.

What context window can glm 4 9b chat 1m use on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, glm 4 9b chat 1m can safely use up to 12K 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 Radeon Pro W7500 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for Radeon Pro W7500 8GBSee all hardware for glm 4 9b chat 1m
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