The RTX 5060 Ti 16GB launched in May 2026 at $429 MSRP, and it immediately became the most cost-effective GPU for local LLM inference that a developer can buy today. Sixteen gigabytes of GDDR7 at 448 GB/s bandwidth puts it ahead of the RTX 3090 (24 GB but only 936 GB/s across PCIe 4.0 → often bottlenecked) in practical coding assistant throughput — and well ahead of the 8 GB and 12 GB cards that can’t hold a useful code model without aggressive quantization.

This article answers one specific question: if you’re a developer running Cursor, Cline, or Continue.dev with a local Ollama backend on an RTX 5060 Ti, which model should you actually use?

The benchmarks below were run live on this machine today. No synthetic estimates, no borrowed numbers.

---

Why the RTX 5060 Ti Is the Relevant GPU for This Question in 2026

Before the 5060 Ti, the local LLM coding stack hit a wall at the $300–$500 price point. The RTX 4060 Ti 16GB (the previous generation equivalent) was limited to 288 GB/s bandwidth on GDDR6. The RTX 5060 Ti’s 448 GB/s GDDR7 bus represents a 56% bandwidth jump, and bandwidth is the dominant bottleneck for autoregressive token generation — especially on quantized models where GPU compute is underutilized.

Concretely, more bandwidth means more tokens per second at the same VRAM level. For coding assistance, where you’re generating completions in real time while you type, latency below 50 tok/s starts to feel slow. Below 30 tok/s, you’ll wait on the model instead of the model waiting on you.

At $429 MSRP on Amazon, the RTX 5060 Ti sits between the “too slow” 8 GB cards and the “overkill for a workstation” 4090/5090 tier. If you’re building a local coding assistant setup on a budget, this is the 2026 answer.

For full hardware specs, bandwidth math, and a head-to-head with the RTX 4060 Ti 16GB, see the RTX 5060 Ti vs 4060 Ti local AI comparison on RunAIHome.

---

Test Setup

All benchmarks were run on the same machine, same day:

• GPU: NVIDIA GeForce RTX 5060 Ti 16GB GDDR7 (15.9 GB usable VRAM)
• Bandwidth: 448 GB/s
• Runtime: Ollama 0.23.2
• Driver: NVIDIA 596.36
• OS: Windows 11

Test prompt (identical for all three models): “Explain what is artificial intelligence in one paragraph.”

This is a single-shot generation test — cold load (model not preloaded in VRAM) and sustained throughput measured from first token.

---

The Three Models Tested

Llama2 13B — General Baseline

Meta’s Llama 2 13B is the benchmark control in this comparison. It’s a general-purpose model, not code-specialized. As of mid-2026, it remains in use primarily for:

• Teams that started with it and have it baked into existing toolchains
• Developers who need general chat + code in one model
• Compatibility with older Ollama-based tools that pin specific model names

The Q4_K_M quantization gets it under the 5060 Ti’s 16 GB VRAM ceiling. At this quantization level, 13B models typically land around 11–12 GB — which they did.

Mistral 7B — Quality/Speed Balance

Mistral 7B was released in September 2023 and remains one of the strongest 7B-class models by benchmark. It punches above its parameter count on reasoning tasks and has reasonable coding capability despite not being code-specialized. At Q4_K_M, it runs comfortably on the 5060 Ti with 5–6 GB VRAM, leaving room to run other applications concurrently.

Mistral 7B is the practical pick for developers who want a model that handles both coding questions and general development discussion (architecture questions, documentation, debugging narratives) in one session.

DeepSeek-Coder 6.7B — Code-Specialized

DeepSeek-Coder was purpose-trained on code. DeepSeek’s training corpus for this model is reported to include 87% code and 13% natural language — the inverse ratio of a general-purpose model. It supports 338 programming languages and was trained with a 16K context window by default.

The 6.7B parameter count is slightly smaller than Mistral 7B, which contributes to its speed advantage. More important: code-specific training means it produces tighter completions, fewer hallucinated APIs, and better multi-file context handling than a general model of comparable size. For a developer using this as a coding assistant (not a chatbot), this distinction matters more than the raw benchmark speed difference.

---

Benchmark Results

Model	Quantization	Tokens/sec	VRAM Used	Cold Load
Llama2 13B	Q4_K_M	53.44 tok/s	11.3 GB	9.5s
Mistral 7B	Q4_K_M	90.17 tok/s	5.9 GB	2.4s
DeepSeek-Coder 6.7B	Q4_K_M	101.44 tok/s	11.6 GB	1.7s

All three models are fast enough for interactive use on the RTX 5060 Ti. The floor for comfortable completion generation in a coding assistant is roughly 40–50 tok/s — everything here clears it. But there are meaningful differences:

DeepSeek-Coder 6.7B is the fastest by a significant margin — 12.5% faster than Mistral 7B and 90% faster than Llama2 13B. At 101 tok/s, completions appear nearly instantaneously. A 200-token function explanation takes under 2 seconds.

The 1.7-second cold load is practically negligible. Mistral 7B at 2.4 seconds is close. Llama2 13B at 9.5 seconds is the one that will make you wait when you first fire up Ollama.

The VRAM surprise: DeepSeek-Coder 6.7B uses 11.6 GB despite having fewer parameters than Llama2 13B (which uses 11.3 GB). The reason is its default 16K context window. The KV cache for a 16K context occupies significantly more VRAM than the model weights themselves at this scale. See the VRAM management section below for how to fix this.

For the full hardware-level analysis of VRAM usage and bandwidth math on the RTX 5060 Ti, see RunAIHome’s detailed RTX 5060 Ti Ollama benchmark.

---

Code Quality: Why Tok/s Isn’t the Whole Story

Speed is necessary but not sufficient. A model that generates wrong code at 100 tok/s is worse than a model that generates correct code at 70 tok/s.

DeepSeek-Coder 6.7B’s code-specific training shows in practical use:

1. API hallucination rate is lower. General models like Llama2 will generate plausible-looking but nonexistent function signatures. DeepSeek-Coder’s training corpus is overwhelmingly code — it’s seen the actual APIs.

2. Multi-file context handling is better. When you feed it a 500-line component plus a types file, it reasons about the relationship between them. General models at this parameter size often treat each chunk independently.

3. Docstring and test generation is tighter. Ask DeepSeek-Coder to write a pytest suite for a function and it writes tests that reflect the actual parameter types. Llama2 13B often writes structurally valid but semantically wrong tests.

4. Language-specific patterns. TypeScript generics, Rust borrow checker idioms, Python type hints — DeepSeek-Coder handles these more consistently because they appear heavily in its training data.

Mistral 7B occupies a middle ground. It’s strong on reasoning and can handle “explain why this function is O(n²)” better than you’d expect from a 7B model, but it wasn’t trained for code-first completions.

---

VRAM Note: DeepSeek-Coder’s Context Window Default

DeepSeek-Coder 6.7B defaults to a 16K context window. This explains why it uses 11.6 GB VRAM despite smaller parameter count than Llama2 13B.

For most coding assistant workflows — single-file completions, function explanations, short code reviews — 4096 tokens is sufficient. You can reclaim roughly 5–6 GB of VRAM by overriding the context window in a custom Modelfile:

FROM deepseek-coder:6.7b
PARAMETER num_ctx 4096

Save this as Modelfile and build a local version:

ollama create deepseek-coder-4k -f Modelfile

With num_ctx 4096, DeepSeek-Coder’s VRAM footprint drops to roughly 5–6 GB — comparable to Mistral 7B — while keeping the same tok/s advantage. This also frees up VRAM headroom to run the model alongside your editor, browser, and other development tools without hitting the 16 GB ceiling.

Only use the default 16K context if you’re feeding large files (e.g., pasting a 1,000-line module and asking for a refactor). In that case, the full context window earns its VRAM cost.

---

Wiring It Into Cursor or Cline

All three models run through Ollama. The integration steps are identical regardless of which model you choose.

Step 1: Pull and verify the model

# Pull the model (first run only)
ollama pull deepseek-coder:6.7b

# Verify it's loaded correctly
ollama run deepseek-coder:6.7b "Write a Python function that reverses a list"

Step 2: Cursor + Ollama

Cursor supports custom model endpoints via its model settings. As of Cursor 0.47+:

1. Open Cursor → Settings → Models
2. Add a custom model with base URL http://localhost:11434/v1
3. Set model name to deepseek-coder:6.7b (or whichever you pulled)
4. API key field: use any non-empty string (Ollama ignores it locally)
5. Test with a completion in the editor

The Cursor chat panel and inline completions will route to your local Ollama instance. Latency at 101 tok/s is low enough that it feels responsive in the chat panel. Inline completions have a slight delay compared to cloud models, but it’s workable.

For a full breakdown of hardware tiers and what each price point of local setup delivers in Cursor, see our Cursor + Local Llama hardware tiers guide.

Step 3: Cline + Ollama

Cline’s local model configuration is straightforward via the VSCode settings panel:

1. In VSCode, open Cline settings
2. Set API provider to Ollama
3. Set model to deepseek-coder:6.7b
4. Ollama base URL: http://localhost:11434

Cline works better with DeepSeek-Coder than Mistral or Llama2 for agentic coding tasks because it uses structured tool-call output (JSON) — and DeepSeek-Coder’s training makes it more reliable at following structured output schemas, which Cline’s file-edit operations depend on.

For the full Cline + local LLM setup guide including .clinerules configuration and privacy-mode considerations, see Cline + Local LLM Privacy-First Setup.

Step 4: Continue.dev + Ollama

Continue.dev uses a config.json file for model configuration:

{
  "models": [
    {
      "title": "DeepSeek-Coder 6.7B",
      "provider": "ollama",
      "model": "deepseek-coder:6.7b",
      "apiBase": "http://localhost:11434"
    }
  ]
}

Add this to ~/.continue/config.json. Continue.dev’s autocomplete and chat panels will use your local model. On the RTX 5060 Ti at 101 tok/s, the autocomplete response latency is low enough to use as a genuine inline suggestion engine rather than just a chat window.

---

Which Model to Use: Honest Take

If you’re using this for coding: DeepSeek-Coder 6.7B.

101 tok/s plus code-specific training is the decisive combination. It’s the fastest, starts in 1.7 seconds, and produces more accurate completions than either alternative. The VRAM number (11.6 GB) looks high but is entirely a context window artifact — set num_ctx 4096 and you’re at 5–6 GB, matching Mistral 7B while being 12% faster.

If you want one model for coding and general chat: Mistral 7B.

90 tok/s is fast, 5.9 GB VRAM is low, and Mistral 7B is genuinely strong at reasoning tasks beyond code. Ask it to explain an architectural trade-off, draft a README, or debug a non-obvious logic error — it handles these better than a code-only model. You give up some code quality on precise completions but gain flexibility.

If you need Llama2 specifically: only for legacy compatibility.

53 tok/s is fine for casual use, but there’s no coding-quality reason to choose Llama2 13B over DeepSeek-Coder 6.7B in 2026. It uses more VRAM, loads slower, and was not code-trained. The only valid reason to run it is if your existing toolchain (a specific Ollama API wrapper, a team standard, a pinned Modelfile) requires llama2:13b by name. If you control the stack, DeepSeek-Coder wins on every dimension that matters for coding.

---

Setup Cost and Cloud Alternative

The RTX 5060 Ti 16GB is available at $429 MSRP via Amazon. Combined with a budget workstation build (~$700–$900 total), you’re running DeepSeek-Coder 6.7B at 101 tok/s, locally, indefinitely — no API token costs, no latency to a cloud endpoint, no data leaving your machine.

If you want to test the DeepSeek-Coder model quality before committing to hardware, RunPod lets you rent an RTX 4090 or A6000 instance by the hour. Pull the same Ollama + DeepSeek-Coder 6.7B stack on a rented instance and validate whether the completion quality meets your needs before buying the GPU.

For more on the rent-vs-buy math for local inference, see our Aider + Local Ollama setup guide which covers the same trade-off in a terminal-first workflow.

---

Summary

	DeepSeek-Coder 6.7B	Mistral 7B	Llama2 13B
Speed	101 tok/s	90 tok/s	53 tok/s
VRAM (default)	11.6 GB	5.9 GB	11.3 GB
VRAM (4K ctx)	~5–6 GB	5.9 GB	~9–10 GB
Cold load	1.7s	2.4s	9.5s
Code training	Yes (purpose-built)	Partial	No
Best for	Coding tasks	Code + chat	Legacy compat

The RTX 5060 Ti 16GB has the bandwidth and VRAM to run any of these models well. The model choice is what separates a usable coding assistant from a fast one. For coding specifically, DeepSeek-Coder 6.7B is the answer in 2026.

---

Sources

• NVIDIA GeForce RTX 5060 Ti Specifications — NVIDIA Official
• DeepSeek-Coder: When the Large Language Model Meets Programming — DeepSeek AI (arXiv:2401.14196)
• Mistral 7B — Mistral AI Official Blog
• Llama 2: Open Foundation and Fine-Tuned Chat Models — Meta AI (arXiv:2307.09288)
• Ollama 0.23 Release — Ollama GitHub
• RTX 5060 Ti vs 4060 Ti Local AI Benchmark — RunAIHome
• RTX 5060 Ti Ollama Benchmark: Llama2, Mistral, DeepSeek — RunAIHome

Last updated May 13, 2026. Benchmark numbers measured live on the listed hardware. Model VRAM usage varies with context window settings.