EXAONE 3.5 7.8B Instruct needs ~31.2 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~14 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
34.4 tok/s
TTFT
5624 ms
Safe context
1.6M
Memory
19.9 GB / 108.8 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 | F | Too heavy | 6.2 tok/s | 17041 ms | 4K |
| Coding | F | Too heavy | 6.2 tok/s | 31242 ms | 4K |
| Agentic Coding | F | Too heavy | 6.2 tok/s | 45443 ms | 4K |
| Reasoning | F | Too heavy | 6.2 tok/s | 36923 ms | 4K |
| RAG | F | Too heavy | 6.2 tok/s | 56804 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for EXAONE 3.5 7.8B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~109 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 109.2 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 109.2 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 109.2 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 109.2 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 109.2 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 109.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 97.5 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 92.5 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 79.4 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 78.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 78.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 50.4 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 49.9 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 46.2 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 41.7 | Offloads |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 40.6 | Fits |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | D39 |
Q3_K_S | 3 | 3.8 GB | Low | D39 |
NVFP4 | 4 | 4.4 GB | Medium | D39 |
Q4_K_M | 4 | 4.8 GB | Medium | D39 |
Q5_K_M | 5 | 5.6 GB | High | D39 |
Q6_K | 6 | 6.4 GB | High | D39 |
Q8_0 | 8 | 8.3 GB | Very High | D39 |
F16Best for your GPU | 16 | 16.0 GB | Maximum | C40 |
Copy-paste commands to run EXAONE 3.5 7.8B Instruct on your machine.
Run
lms load hf-lmstudio-community--exaone-3-5-7-8b-instruct-gguf && lms server startOpciones de mejora
NVIDIA H200 141GBOpción económicaSube la velocidad estimada de decodificación alrededor de un 217%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBMejor relación calidad-precioSube la velocidad estimada de decodificación alrededor de un 217%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
NVIDIA B200 180GBMejora NVIDIASube la velocidad estimada de decodificación alrededor de un 217%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$30,000 MSRP
Yes, NVIDIA DGX Spark 128GB can run EXAONE 3.5 7.8B Instruct at F16 quantization (Runs well). The recommended Q4_K_M requires 6.9 GB which exceeds available memory, but at F16 it needs only 31.2 GB. Expected decode speed: 14.3 tok/s.
EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 6.9 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 31.2 GB.
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 31.2 GB.
On NVIDIA DGX Spark 128GB, EXAONE 3.5 7.8B Instruct achieves approximately 14.3 tokens per second decode speed with a time-to-first-token of 13499ms using F16 quantization.
For coding workloads, EXAONE 3.5 7.8B Instruct on NVIDIA DGX Spark 128GB receives a F grade with 6.2 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, EXAONE 3.5 7.8B Instruct can safely use up to 1.4M tokens of context at F16 quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. NVIDIA DGX Spark 128GB 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.
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