Mac Studio M3 Ultra 96GBGünstige Wahl
96 GB Unified (+48)
C
Adds memory headroom for longer context windows and future model growth.112 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
Can granite 8b code instruct 4k run on Radeon Pro W7900 48GB?
YES — Runs Great
C47Usable
Estimated from fit model
granite 8b code instruct 4k needs ~11.5 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~105 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 11.5 GB, 104.5 tok/s, Runs well
11.5 GB required48.0 GB available
24% VRAM used
Fit status
Runs well
Decode
104.5 tok/s
TTFT
1853 ms
Safe context
639K
Memory
11.5 GB / 48.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom4.8 GB
See how fast it feels
See how fast it feelsgranite 8b code instruct 4k on Radeon Pro W7900 48GB
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: 104.5 tok/s decode · 1.9s TTFT (warm) · 261 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 | 104.5 tok/s | 1011 ms | 639K |
| Coding | C | Runs well | 104.5 tok/s | 1853 ms | 639K |
| Agentic Coding | C | Runs well | 104.5 tok/s | 2696 ms | 639K |
| Reasoning | C | Runs well | 104.5 tok/s | 2190 ms | 639K |
| RAG | C | Runs well | 104.5 tok/s | 3370 ms | 639K |
Inference speed
granite 8b code instruct 4k inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for granite 8b code instruct 4k 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 / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 112.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 112.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 95.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 90.2 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 77.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 49.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 48.7 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 45.1 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 40.7 | Offloads |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 39.6 | Fits |
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 8b code instruct 4k (8B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C41 |
Q3_K_S | 3 | 3.9 GB | Low | C41 |
NVFP4 | 4 | 4.5 GB | Medium | C42 |
Q4_K_M | 4 | 4.9 GB | Medium | C42 |
Q5_K_M | 5 | 5.8 GB | High | C42 |
Q6_K | 6 | 6.6 GB | High | C42 |
Q8_0 | 8 | 8.6 GB | Very High | C42 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C45 |
Get started
Copy-paste commands to run granite 8b code instruct 4k on your machine.
Run
lms load hf-ibm-granite--granite-8b-code-instruct-4k-gguf && lms server startUpgrade-Optionen
Hardware, die granite 8b code instruct 4k gut ausführt
Frequently asked questions
Can Radeon Pro W7900 48GB run granite 8b code instruct 4k?
Yes, Radeon Pro W7900 48GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 104.5 tok/s.
How much VRAM does granite 8b code instruct 4k need?
granite 8b code instruct 4k (8B parameters) requires approximately 11.5 GB of memory with Q4_K_M quantization.
What is the best quantization for granite 8b code instruct 4k?
The recommended quantization for granite 8b code instruct 4k is Q4_K_M, which balances quality and memory efficiency.
What speed will granite 8b code instruct 4k run at on Radeon Pro W7900 48GB?
On Radeon Pro W7900 48GB, granite 8b code instruct 4k achieves approximately 104.5 tokens per second decode speed with a time-to-first-token of 1853ms using Q4_K_M quantization.
Can Radeon Pro W7900 48GB run granite 8b code instruct 4k for coding?
For coding workloads, granite 8b code instruct 4k on Radeon Pro W7900 48GB receives a C grade with 104.5 tok/s and 639K context.
What context window can granite 8b code instruct 4k use on Radeon Pro W7900 48GB?
On Radeon Pro W7900 48GB, granite 8b code instruct 4k can safely use up to 639K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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