RTX 3090 24GBコスパ重視24 GB VRAM (+4)936 GB/s (+576)
C
Raises estimated decode speed by about 95%.112 tok/sデコード
Raises estimated decode speed by about 95%.
〜$1,499 MSRP
Can granite 8b code instruct 4k run on RTX 4000 Ada 20GB?
YES — Runs Great
C50Usable
Estimated from fit model
granite 8b code instruct 4k needs ~9.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~58 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 9.0 GB, 57.5 tok/s, Runs well
9.0 GB required20.0 GB available
45% VRAM used
Fit status
Runs well
Decode
57.5 tok/s
TTFT
3365 ms
Safe context
203K
Memory
9.0 GB / 20.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom2.0 GB
See how fast it feels
See how fast it feelsgranite 8b code instruct 4k on RTX 4000 Ada 20GB
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: 57.5 tok/s decode · 3.4s TTFT (warm) · 144 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 | 57.5 tok/s | 1835 ms | 203K |
| Coding | C | Runs well | 57.5 tok/s | 3365 ms | 203K |
| Agentic Coding | C | Runs well | 57.5 tok/s | 4894 ms | 203K |
| Reasoning | C | Runs well | 57.5 tok/s | 3976 ms | 203K |
| RAG | C | Runs well | 57.5 tok/s | 6117 ms | 203K |
Quantization options
How granite 8b code instruct 4k (8B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C45 |
Q3_K_S | 3 | 3.9 GB | Low | C46 |
NVFP4 | 4 | 4.5 GB | Medium | C46 |
Q4_K_M | 4 | 4.9 GB | Medium | C47 |
Q5_K_M | 5 | 5.8 GB | High | C47 |
Q6_K | 6 | 6.6 GB | High | C48 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | C49 |
F16 | 16 | 16.4 GB | Maximum | F0 |
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 startアップグレードオプション
granite 8b code instruct 4kを快適に動かすハードウェア
RTX 3090 24GBコスパ重視RTX 4090 24GBコスパ最良24 GB VRAM (+4)1008 GB/s (+648)
C
Raises estimated decode speed by about 95%.112 tok/sデコード
Raises estimated decode speed by about 95%.
〜$1,599 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIAアップグレード24 GB VRAM (+4)672 GB/s (+312)
C
Raises estimated decode speed by about 95%.112 tok/sデコード
Raises estimated decode speed by about 95%.
〜$1,599 MSRP
Frequently asked questions
Can RTX 4000 Ada 20GB run granite 8b code instruct 4k?
Yes, RTX 4000 Ada 20GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 57.5 tok/s.
How much VRAM does granite 8b code instruct 4k need?
granite 8b code instruct 4k (8B parameters) requires approximately 9.0 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 RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, granite 8b code instruct 4k achieves approximately 57.5 tokens per second decode speed with a time-to-first-token of 3365ms using Q4_K_M quantization.
Can RTX 4000 Ada 20GB run granite 8b code instruct 4k for coding?
For coding workloads, granite 8b code instruct 4k on RTX 4000 Ada 20GB receives a C grade with 57.5 tok/s and 203K context.
What context window can granite 8b code instruct 4k use on RTX 4000 Ada 20GB?
On RTX 4000 Ada 20GB, granite 8b code instruct 4k can safely use up to 203K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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