MPT-7B-Instruct needs ~14.2 GB VRAM. RX 7700 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~32 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
2.2 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.7 GB host RAM)
Decode
32.0 tok/s
TTFT
6045 ms
Safe context
8K
Memory
14.2 GB / 12.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 60.7 tok/s | 1740 ms | 8K |
| Coding | C | Very compromised (needs ~0.7 GB host RAM) | 32.0 tok/s | 6045 ms | 8K |
| Agentic Coding | F | Too heavy | 12.7 tok/s | 22145 ms | 8K |
| Reasoning | C | Very compromised (needs ~0.7 GB host RAM) | 32.0 tok/s | 7144 ms | 8K |
| RAG | F | Too heavy | 12.7 tok/s | 27682 ms |
How MPT-7B-Instruct (7B params) fits at each quantization level on RX 7700 XT 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B65 |
Q3_K_S | 3 | 3.4 GB | Low | B66 |
NVFP4 | 4 |
Copy-paste commands to run MPT-7B-Instruct on your machine.
Run
lms load mpt-7b-instruct && lms server startUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 48%.
~$349 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 190%.
~$479 MSRP
Yes, RX 7700 XT 12GB can run MPT-7B-Instruct with a C grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 32.0 tok/s.
MPT-7B-Instruct (7B parameters) requires approximately 14.2 GB of memory with Q4_K_M quantization.
The recommended quantization for MPT-7B-Instruct is Q4_K_M, which balances quality and memory efficiency.
On RX 7700 XT 12GB, MPT-7B-Instruct achieves approximately 32.0 tokens per second decode speed with a time-to-first-token of 6045ms using Q4_K_M quantization.
For coding workloads, MPT-7B-Instruct on RX 7700 XT 12GB receives a C grade with 32.0 tok/s and 8K context.
On RX 7700 XT 12GB, MPT-7B-Instruct can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/mpt-7b-instruct-on-rx-7700-xt-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 8K |
3.9 GB |
| Medium |
| B67 |
Q4_K_M | 4 | 4.3 GB | Medium | B67 |
Q5_K_M | 5 | 5.0 GB | High | B68 |
Q6_K | 6 | 5.7 GB | High | B69 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B68 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.