RX 7900 XT 20GBBest value
20 GB VRAM (+4)800 GB/s (+512)
C
Raises estimated decode speed by about 119%.98.4 tok/s decode
Raises estimated decode speed by about 119%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Can granite 8b code instruct 4k run on RTX 2000 Ada 16GB?
YES — Runs Great
C51Usable
○Estimated from fit model
granite 8b code instruct 4k needs ~8.6 GB VRAM. RTX 2000 Ada 16GB has 16.0 GB. With Q4_K_M quantization, expect ~45 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
Share:
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, 44.9 tok/s, Runs well
8.6 GB required16.0 GB available
54% VRAM used
Fit status
Runs well
Decode
44.9 tok/s
TTFT
4316 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 RTX 2000 Ada 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: 44.9 tok/s decode · 4.3s TTFT (warm) · 112 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 | 44.9 tok/s | 2354 ms | 142K |
| Coding | C | Runs well | 44.9 tok/s | 4316 ms | 142K |
| Agentic Coding | C | Runs well | 44.9 tok/s | 6278 ms | 142K |
| Reasoning | C | Runs well | 44.9 tok/s | 5101 ms | 142K |
| RAG | C | Runs well | 44.9 tok/s | 7848 ms | 142K |
Quantization options
How granite 8b code instruct 4k (8B params) fits at each quantization level on RTX 2000 Ada 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 |
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 options
