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Can the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS (8 GB) run GLIDER?

62Good quality (B) · 62 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 49/100 (quality, fit, speed and memory use) sets the ranking.

Barely: it runs very slowly (over GPU memory limit)

Very slow (over GPU memory limit). At Q4_K_M it needs about 4.1 GB of 4.0 GB usable and makes one decision in about 5.8 s.

Best quant
Q4_K_M
Time per decision
5.8 s
Decisions per second
0.2/s
Input assumed
1,024 tokens
Returns
Written feedback, then a score

Best app to run it on the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS (8 GB)

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Splits the model between GPU memory and system RAM, which this setup needs.
  • Gives you direct control of how many layers sit on the GPU, the best way to squeeze a model that doesn’t quite fit.
  • Usually the first to support new model architectures.
llama-server -hf PatronusAI/glider-gguf:Q4_K_M
  • Ollama Splits the model between GPU memory and system RAM, which this setup needs.
  • LocalAI One OpenAI-compatible server for GGUF guard models and rerankers together.

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download GLIDER

Recommended for the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS (8 GB): Q4_K_M. Every other quantization has its own download link in the table below.

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q4_K_M file (opens in a new tab)

huggingface.co/PatronusAI/glider-gguf (opens in a new tab)

ollama run hf.co/PatronusAI/glider-gguf:Q4_K_M

Original weights · Hugging Face

Full-precision files from Patronus AI, for fine-tuning, vLLM, Transformers.

huggingface.co/PatronusAI/glider (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 62Good quality (B) · 62 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 49/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q4_K_M, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Very slowover GPU memory limit
How well it runs on the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS (8 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

PrecisionEditorial precision tier from the model’s published benchmarks, scaled by the quantization or precision used.28.2 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); fewer when it goes past that limit.10.0 / 25
SpeedTime for one decision against a 100 ms target: reading the input plus any tokens the model writes.2.0 / 15
ClarityHow easy the answer is to use in code: a probability or score earns the most, written feedback before a score the least.9.0 / 15
Match score49.2 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS (8 GB)

Quant Memory needed Available Speed Runs Quality Download
Q4_K_M 4.1 GB 4.0 GB 5.8 s Very slowover GPU memory limit 62Good quality (B) · 62 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 49/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 4.4 GB 4.0 GB 7.0 s Very slowfar over GPU memory limit 64Good quality (B) · 64 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 4.9 GB 4.0 GB 8.2 s Very slowfar over GPU memory limit 65Good quality (B) · 65 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking. GGUF files (opens in a new tab)
Q8_0 5.8 GB 4.0 GB — Won't run 66Good quality (B) · 66 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 9.4 GB 4.0 GB — Won't run 66Good quality (B) · 66 of 100: how capable the model is at F16, whatever it runs on. Match score 8/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab) Original weights (opens in a new tab)

Times are for one request with a 1,024-token input and about 150 tokens written. Batching servers process many requests at once.

Other hardware that runs it

Quick answers

How much memory does GLIDER need on the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS 8GB?

At Q4_K_M it needs about 4.1 GB. The AMD Ryzen 7 8845HS / Ryzen 7 255 NAS 8GB has 4.0 GB usable.

How fast is GLIDER on the AMD Ryzen 7 8845HS / Ryzen 7 255 NAS 8GB?

It makes one decision in about 5.8 s at Q4_K_M, an estimate from the hardware and the size of the model.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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