RTX 4000 Ada 20GBコスパ重視20 GB VRAM (+4)360 GB/s (+160)
C
Raises estimated decode speed by about 80%.57.5 tok/sデコード
Raises estimated decode speed by about 80%.
Adds memory headroom for longer context windows and future model growth.
〜$1,250 MSRP
Can granite 8b code instruct 4k run on NVIDIA A2 16GB?
YES — Runs Great
C50Usable
Estimated from fit model
granite 8b code instruct 4k needs ~8.6 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~32 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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) — 8.6 GB, 32.0 tok/s, Runs well
8.6 GB required16.0 GB available
54% VRAM used
Fit status
Runs well
Decode
32.0 tok/s
TTFT
6056 ms
Safe context
142K
Memory
8.6 GB / 16.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsgranite 8b code instruct 4k on NVIDIA A2 16GB
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: 32.0 tok/s decode · 6.1s TTFT (warm) · 80 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 | 32.0 tok/s | 3303 ms | 142K |
| Coding | C | Runs well | 32.0 tok/s | 6056 ms | 142K |
| Agentic Coding | C | Runs well | 32.0 tok/s | 8809 ms | 142K |
| Reasoning | C | Runs well | 32.0 tok/s | 7157 ms | 142K |
| RAG | C | Runs well | 32.0 tok/s | 11011 ms | 142K |
Quantization options
How granite 8b code instruct 4k (8B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C47 |
Q3_K_S | 3 | 3.9 GB | Low | C48 |
NVFP4 | 4 | 4.5 GB | Medium | C48 |
Q4_K_M | 4 | 4.9 GB | Medium | C49 |
Q5_K_M | 5 | 5.8 GB | High | C49 |
Q6_K | 6 | 6.6 GB | High | C50 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | C51 |
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 4000 Ada 20GBコスパ重視RTX 3090 24GBコスパ最良24 GB VRAM (+8)936 GB/s (+736)
C
Raises estimated decode speed by about 250%.112 tok/sデコード
Raises estimated decode speed by about 250%.
Adds memory headroom for longer context windows and future model growth.
〜$1,499 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIAアップグレード24 GB VRAM (+8)672 GB/s (+472)
C
Raises estimated decode speed by about 250%.112 tok/sデコード
Raises estimated decode speed by about 250%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Frequently asked questions
Can NVIDIA A2 16GB run granite 8b code instruct 4k?
Yes, NVIDIA A2 16GB can run granite 8b code instruct 4k with a C grade (Runs well). Expected decode speed: 32.0 tok/s.
How much VRAM does granite 8b code instruct 4k need?
granite 8b code instruct 4k (8B parameters) requires approximately 8.6 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 NVIDIA A2 16GB?
On NVIDIA A2 16GB, granite 8b code instruct 4k achieves approximately 32.0 tokens per second decode speed with a time-to-first-token of 6056ms using Q4_K_M quantization.
Can NVIDIA A2 16GB run granite 8b code instruct 4k for coding?
For coding workloads, granite 8b code instruct 4k on NVIDIA A2 16GB receives a C grade with 32.0 tok/s and 142K context.
What context window can granite 8b code instruct 4k use on NVIDIA A2 16GB?
On NVIDIA A2 16GB, granite 8b code instruct 4k can safely use up to 142K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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