willitrun·ai

Can Granite Code 8B run on RTX 4500 Ada 24GB?

YES — Runs Great

A76Great
Estimated from fit model

Granite Code 8B needs ~10.4 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~75 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) 10.4 GB, 75.2 tok/s, Runs well
10.4 GB required24.0 GB available
43% VRAM used

Fit status

Runs well

Decode

75.2 tok/s

TTFT

2575 ms

Safe context

8K

Memory

10.4 GB / 24.0 GB

Memory breakdown

Weights4.9 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsGranite Code 8B on RTX 4500 Ada 24GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 75.2 tok/s decode · 2.6s TTFT (warm) · 188 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
ChatARuns well75.2 tok/s1405 ms8K
CodingARuns well75.2 tok/s2575 ms8K
Agentic CodingARuns well75.2 tok/s3746 ms8K
ReasoningARuns well75.2 tok/s3043 ms8K
RAGARuns well75.2 tok/s4682 ms8K

Inference speed

Granite Code 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite Code 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 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_M112.0Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M112.0Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M112.0Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M112.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M112.0Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M112.0Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M102.2Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M96.9Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M83.3Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M82.6Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M82.6Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M52.9Fits
NVIDIARTX 3060 12GB
12 GBQ4_K_M52.3Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M48.5Fits
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M42.6Fits
NVIDIARTX 4060 8GB
8 GBQ4_K_M26.6Heavy offload

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 Granite Code 8B (8B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowB70
Q3_K_S
3
3.9 GB
LowA70
NVFP4
4
4.5 GB
MediumA70
Q4_K_M
4
4.9 GB
MediumA71
Q5_K_M
5
5.8 GB
HighA71
Q6_K
6
6.6 GB
HighA72
Q8_0
8
8.6 GB
Very HighA73
F16Best for your GPU
16
16.4 GB
MaximumA75

Get started

Copy-paste commands to run Granite Code 8B on your machine.

Run

ollama run granite-code:8b

Your hardware

More models your RTX 4500 Ada 24GB can run

ModelParamsGradeDecodeCapabilities
AlibabaQwen3-Coder 30B A3B Instruct30.5BS51.6 tok/s
AlibabaQwen 3.5 27B27BS22.4 tok/s
AlibabaQwen 3.6 27B27BS22.4 tok/s
AlibabaQwen3-VL 30B A3B Instruct30BS53.4 tok/s
AlibabaQwen 3.5 9B9BS66.8 tok/s

Frequently asked questions

Can RTX 4500 Ada 24GB run Granite Code 8B?

Yes, RTX 4500 Ada 24GB can run Granite Code 8B with a A grade (Runs well). Expected decode speed: 75.2 tok/s.

How much VRAM does Granite Code 8B need?

Granite Code 8B (8B parameters) requires approximately 10.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 8B?

The recommended quantization for Granite Code 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 8B run at on RTX 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Granite Code 8B achieves approximately 75.2 tokens per second decode speed with a time-to-first-token of 2575ms using Q4_K_M quantization.

Can RTX 4500 Ada 24GB run Granite Code 8B for coding?

For coding workloads, Granite Code 8B on RTX 4500 Ada 24GB receives a A grade with 75.2 tok/s and 8K context.

What context window can Granite Code 8B use on RTX 4500 Ada 24GB?

On RTX 4500 Ada 24GB, Granite Code 8B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

See all results for RTX 4500 Ada 24GBSee all hardware for Granite Code 8B
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