Can the UDOO BOLT V8 (32 GB) run SQLCoder 7B v2?

Yes, but slowly

Slow. At Q4_K_M it needs about 10.3 GB of 16.0 GB usable, with an estimated speed of 6.0 tok/s.

Best quant: Q4_K_M
Generation speed: 6.0 tok/s
Prompt processing: 207 tok/s
First token (1K prompt): 4.9 s
Largest safe context: 16K

Best app to run it on the UDOO BOLT V8 (32 GB)

On Windows

Lemonade

Desktop app · runs on llama.cpp, ONNX Runtime GenAI (Ryzen AI NPU)

AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm.

lemonade pull defog/sqlcoder-7b-2:Q4_K_M

LM Studio Point-and-click: search, download and chat in one window.
llama.cpp Usually the first to support new model architectures.

On Linux

Lemonade

Desktop app · runs on llama.cpp, ONNX Runtime GenAI (Ryzen AI NPU)

AMD’s own app: llama.cpp tuned for Radeon on Vulkan and ROCm.

lemonade pull defog/sqlcoder-7b-2:Q4_K_M

LM Studio Point-and-click: search, download and chat in one window.
llama.cpp Usually the first to support new model architectures.

Want an agent? SQLCoder 7B v2 supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download SQLCoder 7B v2

Quantized files · GGUF

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

huggingface.co/defog/sqlcoder-7b-2 (opens in a new tab)

ollama run hf.co/defog/sqlcoder-7b-2

Original weights · Hugging Face

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

huggingface.co/defog/sqlcoder-7b-2 (opens in a new tab)

No sign-in needed.

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 28Basic quality (D) · 28 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 58/100 (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 Slow: How well it runs on the UDOO BOLT V8 (32 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.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	12.8 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	5.8 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	14.8 / 15
Match score	58.5 / 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 UDOO BOLT V8 (32 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	8.9 GB	16.0 GB	9.9 tok/s	Slow	21Basic quality (D) · 21 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q3_K_M`	9.5 GB	16.0 GB	7.8 tok/s	Slow	25Basic quality (D) · 25 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q4_K_M`	10.3 GB	16.0 GB	6.0 tok/s	Slow	28Basic quality (D) · 28 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q5_K_M`	11.0 GB	16.0 GB	5.0 tok/s	Slow	29Basic quality (D) · 29 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	11.7 GB	16.0 GB	4.2 tok/s	Slow	29Basic quality (D) · 29 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`Q8_0`	13.3 GB	16.0 GB	3.1 tok/s	Slow	30Basic quality (D) · 30 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.	GGUF files (opens in a new tab)
`F16`	19.7 GB	16.0 GB	1.2 tok/s	Very slowfar over GPU memory limit	30Basic quality (D) · 30 of 100: how capable the model is at F16, whatever it runs on. Match score 35/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

30Basic quality (D) · 30 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q8_0 · 51.1 tok/s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER Q8_0 · 62.3 tok/s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER Q8_0 · 68.2 tok/s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at F16, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 F16 · 43.1 tok/s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5060 Ti 16GB Q8_0 · 41.5 tok/s · Runs great
30Basic quality (D) · 30 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5070 Ti Q8_0 · 83.1 tok/s · Runs great

Quick answers

How much memory does SQLCoder 7B v2 need on the UDOO BOLT V8 32GB?

At Q4_K_M it needs about 10.3 GB. The UDOO BOLT V8 32GB has 16.0 GB usable.

How fast is SQLCoder 7B v2 on the UDOO BOLT V8 32GB?

About 6.0 tok/s at Q4_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of SQLCoder 7B v2 should I use on the UDOO BOLT V8 32GB?

Q4_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.