RX 7900 XTX 24GBBest value
960 GB/s (+560)
C
Raises estimated decode speed by about 136%.109.2 tok/s decode
Raises estimated decode speed by about 136%.
~$999 MSRP
EXAONE 3.5 7.8B Instruct i1 needs ~10.0 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~46 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 well
Decode
46.2 tok/s
TTFT
4187 ms
Safe context
244K
Memory
10.0 GB / 23.0 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 46.2 tok/s | 2284 ms | 244K |
| Coding | C | Runs well | 46.2 tok/s | 4187 ms | 244K |
| Agentic Coding | C | Runs well | 46.2 tok/s | 6090 ms | 244K |
| Reasoning | C | Runs well | 46.2 tok/s | 4948 ms | 244K |
| RAG | C | Runs well | 46.2 tok/s | 7613 ms | 244K |
How EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C44 |
Q3_K_S | 3 | 3.8 GB | Low | C45 |
NVFP4 | 4 | 4.4 GB | Medium | C45 |
Q4_K_M | 4 | 4.8 GB | Medium | C45 |
Q5_K_M | 5 | 5.6 GB | High | C46 |
Q6_K | 6 | 6.4 GB | High | C46 |
Q8_0 | 8 | 8.3 GB | Very High | C48 |
F16Best for your GPU | 16 | 16.0 GB | Maximum | C50 |
Copy-paste commands to run EXAONE 3.5 7.8B Instruct i1 on your machine.
Run
lms load hf-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 136%.
~$999 MSRP
Raises estimated decode speed by about 28%.
~$2,499 MSRP
Yes, MacBook Pro M1 Max 32GB can run EXAONE 3.5 7.8B Instruct i1 with a C grade (Runs well). Expected decode speed: 46.2 tok/s.
EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B parameters) requires approximately 10.0 GB of memory with Q4_K_M quantization.
The recommended quantization for EXAONE 3.5 7.8B Instruct i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M1 Max 32GB, EXAONE 3.5 7.8B Instruct i1 achieves approximately 46.2 tokens per second decode speed with a time-to-first-token of 4187ms using Q4_K_M quantization.
For coding workloads, EXAONE 3.5 7.8B Instruct i1 on MacBook Pro M1 Max 32GB receives a C grade with 46.2 tok/s and 244K context.
On MacBook Pro M1 Max 32GB, EXAONE 3.5 7.8B Instruct i1 can safely use up to 244K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M1 Max 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf-on-m1-max-32gb" 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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