Top Ollama Coding Models: Q4 2025

Local AI has reached critical mass. The best open models now rival GPT-4 for coding tasks. Zero monthly fees. Complete data sovereignty. The decision framework is simple: deploy now or lose competitive advantage.

The numbers that matter

Performance reality check:

• Local latency: Sub-100ms responses vs 500ms+ cloud latency
• Cost structure: One-time hardware investment vs $100-300/month subscriptions
• Privacy guarantee: 100% air-gapped capability
• Context windows: Up to 128K tokens on consumer hardware
• Language support: 80-600+ languages depending on model

Continue’s validated stack:

• Chat: Qwen2.5-Coder 7B, DeepSeek-R1 32B, Llama3.1 8B
• Autocomplete: Qwen2.5-Coder 1.5B, StarCoder2 3B
• Advanced reasoning: DeepSeek-R1 32B with tool support
• Embeddings: Nomic-embed-text for codebase indexing

The elite five: Continue recommended models

1. Qwen2.5-Coder: Continue’s top pick

Why Continue recommends it:

• Autocomplete champion: 1.5B model specifically recommended by Continue
• Chat powerhouse: 7B variant excels at code generation
• Context window: 128K tokens
• Languages: 80+ supported
• Inference: 60-80 tokens/second on RTX 4070

Deployment matrix:

• Sizes: 0.5B, 1.5B, 3B, 7B, 14B, 32B
• Memory: 4GB (1.5B), 8GB (7B), 16GB (14B)
• Continue config: Pre-validated model blocks available

Production configuration:

models:
  - name: Qwen 2.5 Coder Chat
    provider: ollama
    model: qwen2.5-coder:7b
    roles:
      - chat
      - edit
  - name: Qwen 2.5 Coder Autocomplete
    provider: ollama
    model: qwen2.5-coder:1.5b
    roles:
      - autocomplete

2. DeepSeek-R1: Advanced reasoning specialist

Continue integration:

• Native tool support in Continue
• Thinking mode for complex problems
• Context: 128K tokens
• Best for: System design, debugging, refactoring

Performance profile:

• Advanced chain-of-thought reasoning
• 32B model runs on 32GB RAM
• Controllable reasoning transparency

Continue configuration:

models:
  - name: DeepSeek R1 32B
    provider: ollama
    model: deepseek-r1:32b
    roles:
      - chat
      - edit
    capabilities:
      - tool_use

3. DeepSeek-Coder V2: Mathematical prowess

Technical strengths:

• Superior mathematical reasoning
• Languages: 338 supported
• Architecture: Mixture-of-Experts efficiency
• Context window: 128K tokens

Continue recommendation:

• 16B variant for balanced performance
• Superior for algorithmic challenges
• Excellent debugging capabilities

Deployment specs:

• Sizes: 6.7B, 16B, 33B
• Memory: 8GB (6.7B), 16GB (16B)
• Quantization: Q4_K_M recommended

4. CodeLlama: Production reliability

Why teams choose it:

• Battle-tested in thousands of organizations
• Consistent, predictable outputs
• Meta’s ongoing support commitment
• Specialized Python variant available

Model variants:

• Base: General programming
• Python: Web development specialist
• Instruct: Following complex instructions
• Sizes: 7B, 13B, 34B, 70B

Continue setup:

models:
  - name: CodeLlama 13B
    provider: ollama
    model: codellama:13b
    roles:
      - chat
      - edit

5. StarCoder2: Lightweight efficiency

Continue’s autocomplete alternative:

• 3B model recommended for autocomplete
• 600+ programming languages
• Exceptional efficiency for size
• Transparent training data

Deployment advantages:

• Minimal memory: 4GB for 3B model
• Fast inference: 100+ tokens/second
• COBOL and legacy language support

Hardware reality: what actually works

Minimum viable setup (8-16GB RAM)

Continue-recommended models:

• Qwen2.5-Coder 1.5B (autocomplete)
• Llama3.1 8B or Mistral 7B (chat)
• Performance: 80-120 tokens/second autocomplete

Professional developer (32GB RAM + RTX 4070)

Optimal configuration:

• Qwen2.5-Coder 7B (primary)
• Qwen2.5-Coder 1.5B (autocomplete)
• DeepSeek-Coder 6.7B (alternative)
• Performance: 45-80 tokens/second

Team deployment (64GB+ RAM + RTX 4090)

Maximum capability:

• DeepSeek-R1 32B (reasoning)
• CodeLlama 34B (reliability)
• Multiple model switching
• Concurrent user support: 5-10 developers

Deployment: Continue’s proven path

Step 1: Install Ollama (2 minutes)

# macOS/Linux
curl -fsSL https://ollama.com/install.sh | sh

# Pull Continue's recommended models
ollama pull qwen2.5-coder:7b
ollama pull qwen2.5-coder:1.5b
ollama pull nomic-embed-text

Step 2: Configure Continue (3 minutes)

{
  "models": [
    {
      "name": "Qwen 2.5 Coder 7B",
      "provider": "ollama",
      "model": "qwen2.5-coder:7b",
      "roles": ["chat", "edit"]
    }
  ],
  "autocompleteModel": {
    "name": "Qwen 2.5 Coder 1.5B",
    "provider": "ollama",
    "model": "qwen2.5-coder:1.5b",
    "roles": ["autocomplete"]
  },
  "embeddingsProvider": {
    "provider": "ollama",
    "model": "nomic-embed-text"
  }
}

Step 3: Performance optimization

# Enable optimizations
export OLLAMA_NUM_PARALLEL=4
export OLLAMA_FLASH_ATTENTION=1
export OLLAMA_MAX_LOADED_MODELS=2

Model selection matrix

By use case

Code generation:

• Primary: Qwen2.5-Coder 7B
• Alternative: CodeLlama 13B
• Heavy: DeepSeek-Coder 16B

Autocomplete:

• Fast: Qwen2.5-Coder 1.5B
• Alternative: StarCoder2 3B
• Thinking models: Disable thinking mode

Complex reasoning:

• DeepSeek-R1 32B (with tool support)
• Llama3.1 8B (with tool support)
• DeepSeek-Coder 33B (mathematical)

By hardware constraints

8GB VRAM:

• Qwen2.5-Coder 1.5B
• Mistral 7B
• Llama3.1 8B (Q4_K_M)

16GB VRAM:

• Qwen2.5-Coder 7B
• DeepSeek-Coder 16B
• CodeLlama 13B

24GB+ VRAM:

• DeepSeek-R1 32B
• CodeLlama 34B
• Multiple models loaded

Performance tuning playbook

Quantization strategy

Continue’s recommendations:

• Q4_K_M: Best balance (default)
• Q5_K_M: Quality priority
• Q3_K_M: Memory constrained
• Q8_0: Maximum quality

Context optimization

Proven configurations:

• Autocomplete: 2K-4K tokens (speed)
• Chat: 16K-32K tokens (balance)
• Analysis: 64K-128K tokens (depth)

Temperature settings

requestOptions:
  temperature: 0.2  # Code generation
  temperature: 0.5  # Exploration
  temperature: 0.0  # Deterministic output

ROI calculation

Cloud services (annual)

• GitHub Copilot: $1,200/developer
• Claude Pro: $2,400/developer
• GPT-4 API: $1,800-3,600/developer
• Total: $1,200-3,600/developer/year

Local deployment (one-time)

• Hardware: $1,500-3,000
• Electricity: $120/year
• Breakeven: 6-12 months
• 3-year savings: $3,600-10,800/developer

Productivity gains

• Zero latency: 20-30% speed improvement
• Unlimited usage: No throttling anxiety
• Custom models: Domain-specific optimization
• Privacy: Enable work on sensitive code

Team deployment strategies

Startup configuration

Models:

• Qwen2.5-Coder 7B (primary)
• Qwen2.5-Coder 1.5B (autocomplete)

Hardware:

• Single RTX 4070, 32GB RAM
• Supports 2-4 developers
• Cost: $2,000 total investment

Enterprise deployment

Models:

• CodeLlama 34B (production)
• DeepSeek-R1 32B (reasoning)
• Qwen2.5-Coder suite (all sizes)

Infrastructure:

• Kubernetes cluster
• Multiple RTX 4090s
• Load balancing
• Model routing by task type

Individual developer

Quick start:

• Qwen2.5-Coder 7B only
• RTX 4060, 16GB RAM
• 10-minute setup
• Immediate productivity boost

Integration ecosystem

IDE support (Continue validated)

Tier 1:

• Visual Studio Code
• JetBrains IDEs (all)
• Cursor
• Neovim (with plugins)

Web interfaces:

• Open WebUI (team deployments)
• Continue Chat UI
• Custom API endpoints

Advanced integrations

• LangChain (RAG pipelines)
• LlamaIndex (document processing)
• Docker/Kubernetes (scaling)
• CI/CD pipelines (automated testing)

Common pitfalls and solutions

”Model loads slowly”

• Use OLLAMA_MAX_LOADED_MODELS=1
• Implement model switching logic
• Pre-load frequently used models

”Autocomplete feels laggy”

• Switch to Qwen2.5-Coder 1.5B
• Reduce context to 2K tokens
• Enable Flash Attention

”Running out of memory”

• Use aggressive quantization (Q3_K_M)
• Reduce context window
• Implement model offloading

The competitive reality

Market signals:

• Continue: 500K+ active users
• Ollama: 100K+ GitHub stars
• Enterprise adoption: 200% YoY growth
• Microsoft/Google: Investing in local AI

Your decision framework:

1. Competitors using local AI have zero API costs
2. They own their code and AI interactions
3. They customize models to their domain
4. They work offline with zero friction

The gap widens daily. Organizations clinging to cloud-only AI face mounting costs, privacy risks, and competitive disadvantage. Local deployment isn’t experimental—it’s the new standard.

Action plan: next 24 hours

Hour 1:

• Install Ollama
• Pull Qwen2.5-Coder 7B and 1.5B
• Configure Continue

Hour 2-4:

• Test on real codebase
• Benchmark against current solution
• Document performance metrics

Hour 5-24:

• Fine-tune configuration
• Share with team
• Calculate specific ROI

Week 1:

• Pilot with 2-3 developers
• Gather feedback
• Optimize for workflow

Resources

Essential links:

• Continue Ollama Guide
• Continue Model Recommendations
• Continue Model Hub

The binary choice: Pay increasing cloud costs with privacy risks, or deploy local models with superior control, zero recurring costs, and unlimited usage.

Every day without local AI is a day of competitive disadvantage.

Deploy your first model now →