Can Solar Open 100B i1 run on Radeon Pro W7500 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Solar Open 100B i1 needs ~74.4 GB but Radeon Pro W7500 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 74.4 GB, exceeds 8.0 GB available

74.4 GB required8.0 GB available

930% VRAM needed

66.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

74.4 GB / 8.0 GB

Offload

90%

Memory breakdown

Weights61.0 GB

KV Cache11.7 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsSolar Open 100B i1 on Radeon Pro W7500 8GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 74.4 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Inference speed

Solar Open 100B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Solar Open 100B i1 at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~10 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?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	9.8	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	9.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	7.6	Tight
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	7.2	Tight
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	5.1	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	4.9	Too big
RTX 5090 32GB	32 GB	Q4_K_M	3.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.8	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.7	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.5	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.2	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.0	Too big

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.

Quantization options

How Solar Open 100B i1 (100B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	39.0 GB	Low	F0
Q3_K_S	3	49.0 GB	Low	F0
NVFP4	4	56.0 GB	Medium	F0
Q4_K_M	4	61.0 GB	Medium	F0
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

Upgrade-Optionen

Hardware, die Solar Open 100B i1 gut ausführt

AMD Instinct MI350X 288GBGünstige Wahl

288 GB VRAM (+280)8000 GB/s (+7776)

Makes the model fit on the accelerator instead of staying completely out of reach.95.7 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $8,000 MSRP

AMD Instinct MI300A 128GBBestes Preis-Leistungs-Verhältnis

128 GB VRAM (+120)5300 GB/s (+5076)

Makes the model fit on the accelerator instead of staying completely out of reach.60.8 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $12,000 MSRP

AMD Instinct MI250X 128GBAMD-Upgrade

128 GB VRAM (+120)3200 GB/s (+2976)

Makes the model fit on the accelerator instead of staying completely out of reach.40.9 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $15,000 MSRP

Frequently asked questions

Can Radeon Pro W7500 8GB run Solar Open 100B i1?

No, Solar Open 100B i1 requires more memory than Radeon Pro W7500 8GB provides.

How much VRAM does Solar Open 100B i1 need?

Solar Open 100B i1 (100B parameters) requires approximately 74.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Solar Open 100B i1?

The recommended quantization for Solar Open 100B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Solar Open 100B i1 run at on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, Solar Open 100B i1 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can Radeon Pro W7500 8GB run Solar Open 100B i1 for coding?

For coding workloads, Solar Open 100B i1 on Radeon Pro W7500 8GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Solar Open 100B i1 use on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, Solar Open 100B i1 can safely use up to 4K 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 i1 feels slow on Radeon Pro W7500 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for Radeon Pro W7500 8GB See all hardware for Solar Open 100B i1

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