MacBook Pro M4 Max 48GBGünstige Wahl
48 GB Unified (+16)
C
This setup is broadly balanced for this model.76.8 tok/s Dekodierung
ca. $2,499 MSRP
Can granite 8b code instruct 4k run on Radeon AI PRO R9700 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
granite 8b code instruct 4k needs ~9.9 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~77 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 9.9 GB, 77.4 tok/s, Runs well
9.9 GB required32.0 GB available
31% VRAM used
Fit status
Runs well
Decode
77.4 tok/s
TTFT
2502 ms
Safe context
393K
Memory
9.9 GB / 32.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsgranite 8b code instruct 4k on Radeon AI PRO R9700 32GB
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: 77.4 tok/s decode · 2.5s TTFT (warm) · 193 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 | 77.4 tok/s | 1365 ms | 393K |
| Coding | C | Runs well | 77.4 tok/s | 2502 ms | 393K |
| Agentic Coding | C | Runs well | 77.4 tok/s | 3639 ms | 393K |
| Reasoning | C | Runs well | 77.4 tok/s | 2957 ms | 393K |
| RAG | C | Runs well | 77.4 tok/s | 4549 ms | 393K |
Quantization options
How granite 8b code instruct 4k (8B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C43 |
Q3_K_S | 3 | 3.9 GB | Low | C43 |
NVFP4 | 4 | 4.5 GB | Medium | C43 |
Q4_K_M | 4 | 4.9 GB | Medium | C44 |
Q5_K_M | 5 | 5.8 GB | High | C44 |
Q6_K | 6 | 6.6 GB | High | C44 |
Q8_0 | 8 | 8.6 GB | Very High | C45 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C49 |
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 AI PRO R9700 32GB run granite 8b code instruct 4k?
Yes, Radeon AI PRO R9700 32GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 77.4 tok/s.
How much VRAM does granite 8b code instruct 4k need?
granite 8b code instruct 4k (8B parameters) requires approximately 9.9 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 AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, granite 8b code instruct 4k achieves approximately 77.4 tokens per second decode speed with a time-to-first-token of 2502ms using Q4_K_M quantization.
Can Radeon AI PRO R9700 32GB run granite 8b code instruct 4k for coding?
For coding workloads, granite 8b code instruct 4k on Radeon AI PRO R9700 32GB receives a C grade with 77.4 tok/s and 393K context.
What context window can granite 8b code instruct 4k use on Radeon AI PRO R9700 32GB?
On Radeon AI PRO R9700 32GB, granite 8b code instruct 4k can safely use up to 393K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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