Supported Models

Cortensor runs a curated catalog of open-weight LLMs across two primary engines:

• Llamafile – quantized GGUF models (CPU-friendly, can also use GPU where available)

• Ollama – full GPU models (for higher-end / dedicated GPU miners)

Internally, each model is packaged as a Docker image identified as:

• cts-llm, cts-llm-1, cts-llm-2, … cts-llm-40

These cts-llm-* IDs are Docker image tags, not the raw model names. Each image wraps one runtime engine (llamafile or Ollama) and one concrete model identifier (e.g. deepseek-r1:8b-gpu).

❗ This catalog will grow over time. New images will be added with higher IDs (cts-llm-41, cts-llm-42, …), so treat the tables below as versioned snapshots rather than a permanent, fixed list.

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Quantized Models (Llamafile)

Engine: llamafile Indexes: 0–11 Hardware: CPU-first, can use GPU where available Purpose: Maximum coverage across heterogeneous nodes (CPUs, smaller GPUs, edge machines) using 4–8 bit quantization.

These images are ideal for:

• Nodes without a dedicated GPU

• Mixed-resource environments (small VPS, home PCs, edge hardware)

• High fan-out / broad geographic coverage

Model Families Covered

The current llamafile set includes quantized versions of:

• Vision / Multimodal

  • llava-v1.5-7b-q4 (used as default-gpu on many nodes)

• Reasoning & General LLMs

  • DeepSeek R1 Distill Llama 8B (Q4)

  • Meta Llama 3.1 8B Instruct (Q4)

  • Mistral 7B Instruct v0.3 (Q4)

  • Granite 3.2 8B Instruct (Q4)

  • Qwen2.5 7B Instruct 1M (Q4)

• High-Capacity & Coder Variants

  • Qwen QwQ 32B (Q4)

  • Qwen3 4B (Q8)

  • Gemma 3 4B / 12B IT (Q6 / Q4)

  • DeepSeek R1 Distill Qwen 14B (Q4)

  • Qwen2.5 Coder 14B Instruct (Q4)

These quantized models prioritize:

• Lower memory footprint

• Broad participation from smaller nodes

• “Good enough” quality for many general-purpose workloads and background jobs

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Full GPU Models (Ollama)

Engine: ollama Indexes: 12–40 Hardware: GPU nodes (NVIDIA strongly preferred) Purpose: High-quality, low-latency inference for more demanding workloads and higher-SLA routes.

These are full GPU models (not llamafile-style Q4/Q6 GGUFs). They are meant for:

• Dedicated or strong GPU miners

• Latency-sensitive user requests

• High-quality routing tiers

Model Families Covered

The current Ollama set spans several major model families:

• Gemma 3

  • 270M, 4B, 12B, 27B

• DeepSeek R1

  • 8B, 14B, 70B

• GPT-OSS

  • 20B, 120B

• Granite

  • Granite 4 3B

• Ministral / Mistral

  • Ministral 3 8B / 14B

  • Mistral 7B

• Qwen 3 & Qwen 3 VL

  • Qwen3 4B / 8B

  • Qwen3 VL 4B / 8B

• Phi

  • Phi-3 3.8B / 14B

  • Phi-4 14B

• Llama 3.x

  • Llama 3.2 1B / 3B

  • Llama 3.1 8B / 70B

• Others

  • Dolphin 3 8B

  • TinyLLaMA 1.1B

  • Falcon 3 3B / 7B / 10B

These are best suited to:

• Nodes with enough VRAM to serve these models efficiently

• High-SLA task classes

• Scenarios where quality/latency > footprint

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How the Mapping Works

At the node / Docker level, you mostly deal with:

• cts-llm-* → Docker image ID

• Each image contains:

  • The engine (llamafile or ollama)

  • The model identifier (e.g. deepseek-r1:8b-gpu, Qwen2.5-7B-Instruct-1M-Q4_K_M)

At the network / session level, Cortensor can:

• Route tasks by model index (0–40)

• Or use more descriptive labels via config/SDKs where needed

As we add more models, we’ll extend the table with new indices and images; this section stays as the canonical mapping.

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Appendix: Full Model Mapping

Below are the current model mappings used by Cortensor.

🧩 Columns

• Index – numeric model index used in configs / selection

• Docker Image ID – the cts-llm-* tag

• Engine – llamafile or ollama

• Model Identifier – actual model/runtime name inside the image

• Model Family / Size – human-readable description

• Quantized? – whether it’s a quantized GGUF (for llamafile) or full GPU model

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Quantized Models – Llamafile (0–11)

Index	Docker Image ID	Engine	Model Identifier	Model Family / Size	Quantized?
0	cts-llm	llamafile	default default-gpu (llamafile)	LLaVA v1.5 7B Q4	Yes – 4-bit
1	cts-llm-1	llamafile	DeepSeek-R1-Distill-Llama-8B-Q4_K_M	DeepSeek R1 Distill Llama 8B	Yes – 4-bit
2	cts-llm-2	llamafile	Meta-Llama-3.1-8B-Instruct.Q4_K_M	Llama 3.1 8B Instruct	Yes – 4-bit
3	cts-llm-3	llamafile	Qwen_QwQ-32B-Q4_K_M	Qwen QwQ 32B	Yes – 4-bit
4	cts-llm-4	llamafile	Mistral-7B-Instruct-v0.3.Q4_0	Mistral 7B Instruct v0.3	Yes – 4-bit
5	cts-llm-5	llamafile	granite-3.2-8b-instruct-Q4_K_M	Granite 3.2 8B Instruct	Yes – 4-bit
6	cts-llm-6	llamafile	Qwen2.5-7B-Instruct-1M-Q4_K_M	Qwen2.5 7B Instruct 1M	Yes – 4-bit
7	cts-llm-7	llamafile	google_gemma-3-4b-it-Q6_K	Gemma 3 4B IT	Yes – 6-bit
8	cts-llm-8	llamafile	Qwen_Qwen3-4B-Q8_0	Qwen3 4B	Yes – 8-bit
9	cts-llm-9	llamafile	google_gemma-3-12b-it-Q4_K_M	Gemma 3 12B IT	Yes – 4-bit
10	cts-llm-10	llamafile	DeepSeek-R1-Distill-Qwen-14B-Q4_K_M	DeepSeek R1 Distill Qwen 14B	Yes – 4-bit
11	cts-llm-11	llamafile	Qwen2.5-Coder-14B-Instruct-Q4_K_M	Qwen2.5 Coder 14B Instruct	Yes – 4-bit

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Full GPU Models – Ollama (12–40)

Index	Docker Image ID	Engine	Model Identifier	Model Family / Size	Quantized?
12	cts-llm-12	ollama	gemma3:270m-gpu	Gemma 3 270M	No – full GPU
13	cts-llm-13	ollama	gpt-oss:20b-gpu	GPT-OSS 20B	No – full GPU
14	cts-llm-14	ollama	deepseek-r1:8b-gpu	DeepSeek R1 8B	No – full GPU
15	cts-llm-15	ollama	granite4:3b-gpu	Granite 4 3B	No – full GPU
16	cts-llm-16	ollama	gpt-oss:120b-gpu	GPT-OSS 120B	No – full GPU
17	cts-llm-17	ollama	deepseek-r1:70b-gpu	DeepSeek R1 70B	No – full GPU
18	cts-llm-18	ollama	gemma3:4b-gpu	Gemma 3 4B	No – full GPU
19	cts-llm-19	ollama	gemma3:12b-gpu	Gemma 3 12B	No – full GPU
20	cts-llm-20	ollama	ministral-3:8b-gpu	Ministral 3 8B	No – full GPU
21	cts-llm-21	ollama	deepseek-r1:14b-gpu	DeepSeek R1 14B	No – full GPU
22	cts-llm-22	ollama	gemma3:27b-gpu	Gemma 3 27B	No – full GPU
23	cts-llm-23	ollama	qwen3-vl:4b-gpu	Qwen3 VL 4B	No – full GPU (vision-capable)
24	cts-llm-24	ollama	qwen3-vl:8b-gpu	Qwen3 VL 8B	No – full GPU (vision-capable)
25	cts-llm-25	ollama	ministral-3:14b-gpu	Ministral 3 14B	No – full GPU
26	cts-llm-26	ollama	qwen3:4b-gpu	Qwen3 4B	No – full GPU
27	cts-llm-27	ollama	qwen3:8b-gpu	Qwen3 8B	No – full GPU
28	cts-llm-28	ollama	mistral:7b-gpu	Mistral 7B	No – full GPU
29	cts-llm-29	ollama	phi3:3.8b-gpu	Phi-3 3.8B	No – full GPU
30	cts-llm-30	ollama	phi3:14b-gpu	Phi-3 14B	No – full GPU
31	cts-llm-31	ollama	llama3:1b-gpu	Llama 3.2 1B	No – full GPU
32	cts-llm-32	ollama	llama3:3b-gpu	Llama 3.2 3B	No – full GPU
33	cts-llm-33	ollama	llama3:8b-gpu	Llama 3.1 8B	No – full GPU
34	cts-llm-34	ollama	llama3:70b-gpu	Llama 3.1 70B	No – full GPU
35	cts-llm-35	ollama	phi4:14b-gpu	Phi-4 14B	No – full GPU
36	cts-llm-36	ollama	dolphin3:8b-gpu	Dolphin 3 8B	No – full GPU
37	cts-llm-37	ollama	tinyllama1.1b-gpu	TinyLLaMA 1.1B	No – full GPU
38	cts-llm-38	ollama	falcon3:3b-gpu	Falcon 3 3B	No – full GPU
39	cts-llm-39	ollama	falcon3:7b-gpu	Falcon 3 7B	No – full GPU
40	cts-llm-40	ollama	falcon3:10b-gpu	Falcon 3 10B	No – full GPU