Raises estimated decode speed by about 61%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
granite 8b code instruct 4k needs ~7.8 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~30 tok/s.
Operating mode
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
Fit status
Runs with offload
Decode
30.3 tok/s
TTFT
6390 ms
Safe context
19K
Memory
7.8 GB / 8.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 30.3 tok/s | 3486 ms | 19K |
| Coding | C | Runs with offload | 30.3 tok/s | 6390 ms | 19K |
| Agentic Coding | D | Very compromised (needs ~0.4 GB host RAM) | 18.8 tok/s | 14985 ms | 19K |
| Reasoning | C | Runs with offload | 30.3 tok/s | 7552 ms | 19K |
| RAG | D | Very compromised (needs ~0.4 GB host RAM) | 18.8 tok/s | 18731 ms | 19K |
How granite 8b code instruct 4k (8B params) fits at each quantization level on RTX 3050 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | C53 |
NVFP4 | 4 | 4.5 GB | Medium | C53 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
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 startOpções de upgrade
Raises estimated decode speed by about 61%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Raises estimated decode speed by about 88%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Raises estimated decode speed by about 42%.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
Yes, RTX 3050 8GB can run granite 8b code instruct 4k with a C grade (Runs with offload). Expected decode speed: 30.3 tok/s.
granite 8b code instruct 4k (8B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.
The recommended quantization for granite 8b code instruct 4k is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 8GB, granite 8b code instruct 4k achieves approximately 30.3 tokens per second decode speed with a time-to-first-token of 6390ms using Q4_K_M quantization.
For coding workloads, granite 8b code instruct 4k on RTX 3050 8GB receives a C grade with 30.3 tok/s and 19K context.
On RTX 3050 8GB, granite 8b code instruct 4k can safely use up to 19K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-ibm-granite--granite-8b-code-instruct-4k-gguf-on-rtx-3050-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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