RTX PRO 6000 Blackwell Workstation Edition 96GBGünstige Wahl96 GB VRAM (+16)
C
This setup is broadly balanced for this model.24.7 tok/s Dekodierung
ca. $9,999 MSRP
Can Solar Open 100B run on NVIDIA A100 80GB?
YES — With Offload
C49Usable
Estimated from fit model
Solar Open 100B needs ~81.6 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~23 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Balanced
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) — 81.6 GB, 23.2 tok/s, Runs with offload (needs ~1.2 GB host RAM)
81.6 GB required80.0 GB available
102% VRAM needed
1.6 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~1.2 GB host RAM)
Decode
23.2 tok/s
TTFT
8356 ms
Safe context
14K
Memory
81.6 GB / 80.0 GB
Memory breakdown
Weights61.0 GB
KV Cache11.7 GB
Runtime0.9 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsSolar Open 100B on NVIDIA A100 80GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 23.2 tok/s decode · 8.4s TTFT (warm) · 58 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 28.1 tok/s | 3761 ms | 14K |
| Coding | C | Runs with offload (needs ~1.2 GB host RAM) | 23.2 tok/s | 8356 ms | 14K |
| Agentic Coding | D | Very compromised (needs ~8.7 GB host RAM) | 18.6 tok/s | 15165 ms | 14K |
| Reasoning | C | Runs with offload (needs ~1.2 GB host RAM) | 23.2 tok/s | 9875 ms | 14K |
| RAG | D | Very compromised (needs ~8.7 GB host RAM) | 18.6 tok/s | 18956 ms | 14K |
Quantization options
How Solar Open 100B (100B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 39.0 GB | Low | C47 |
Q3_K_S | 3 | 49.0 GB | Low | C48 |
NVFP4 | 4 | 56.0 GB | Medium | C48 |
Q4_K_MBest for your GPU | 4 | 61.0 GB | Medium | C48 |
Q5_K_M | 5 | 72.0 GB | High | F0 |
Q6_K | 6 | 82.0 GB | High | F0 |
Q8_0 | 8 | 107.0 GB | Very High | F0 |
F16 | 16 | 205.0 GB | Maximum | F0 |
Get started
Copy-paste commands to run Solar Open 100B on your machine.
Run
lms load hf-aaryank--solar-open-100b-gguf && lms server startUpgrade-Optionen
Hardware, die Solar Open 100B gut ausführt
RTX PRO 6000 Blackwell Workstation Edition 96GBGünstige WahlNVIDIA H20 96GBBestes Preis-Leistungs-Verhältnis96 GB VRAM (+16)4000 GB/s (+1961)
C
Raises estimated decode speed by about 129%.53.1 tok/s Dekodierung
Raises estimated decode speed by about 129%.
ca. $12,000 MSRP
NVIDIA H200 141GBNVIDIA-Upgrade141 GB VRAM (+61)4800 GB/s (+2761)
C
Raises estimated decode speed by about 185%.66.1 tok/s Dekodierung
Raises estimated decode speed by about 185%.
Adds memory headroom for longer context windows and future model growth.
ca. $30,000 MSRP
Frequently asked questions
Can NVIDIA A100 80GB run Solar Open 100B?
Yes, NVIDIA A100 80GB can run Solar Open 100B with a C grade (Runs with offload (needs ~1.2 GB host RAM)). Expected decode speed: 23.2 tok/s.
How much VRAM does Solar Open 100B need?
Solar Open 100B (100B parameters) requires approximately 81.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Solar Open 100B?
The recommended quantization for Solar Open 100B is Q4_K_M, which balances quality and memory efficiency.
What speed will Solar Open 100B run at on NVIDIA A100 80GB?
On NVIDIA A100 80GB, Solar Open 100B achieves approximately 23.2 tokens per second decode speed with a time-to-first-token of 8356ms using Q4_K_M quantization.
Can NVIDIA A100 80GB run Solar Open 100B for coding?
For coding workloads, Solar Open 100B on NVIDIA A100 80GB receives a C grade with 23.2 tok/s and 14K context.
What context window can Solar Open 100B use on NVIDIA A100 80GB?
On NVIDIA A100 80GB, Solar Open 100B can safely use up to 14K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Solar Open 100B feels slow on NVIDIA A100 80GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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