# Best LLM Models for RTX 5060 TI (16GB VRAM) — RAG/Q&A Quality

## Current Setup Baseline
- **Primary:** `qwen2.5-coder-32b-instruct` at IQ2_M (~13GB, 4.8 tok/s)
- **Fallback:** `dolphin-llama3:8b` (~30 tok/s)

**Problem with current setup:** The 32B model is aggressively quantized (IQ2_M = ~2-bit) to fit in 16GB, severely degrading quality. At 4.8 tok/s, it's also painfully slow. You're sacrificing both quality AND speed for parameter count.

## Hardware Context: RTX 5060 TI
- 16GB GDDR7, 448 GB/s memory bandwidth
- 4,608 CUDA cores, 144 tensor cores (5th gen)
- 759 AI TOPS (INT8/FP8), ~380 TOPS (INT4/FP4)
- Native FP4/FP8 support (Blackwell architecture)
- ~80-85% performance of RTX 3090 at FP16, at half the TDP

**Key insight:** Models that fit **entirely** in VRAM are dramatically faster than those requiring CPU offload. The boundary is ~14GB for model weights to leave room for KV cache.

---

## TIER 1: TOP RECOMMENDATIONS (Best Quality/Speed for 16GB)

### 1. Qwen3 14B (Q4_K_M) — BEST OVERALL REPLACEMENT
- **Ollama:** `ollama pull qwen3:14b`
- **Params:** 14B dense
- **Quantization:** Q4_K_M (~9.2GB model, fits entirely in VRAM)
- **Speed (5060 TI estimated):** 33-40 tok/s generation, ~943 tok/s prompt processing (at 16K ctx)
- **Context:** Up to 45K tokens in 16GB (32K comfortable)
- **Quality:** Surpasses Qwen2.5-32B on reasoning/coding benchmarks; 85.5 ArenaHard; hybrid thinking/non-thinking modes
- **Reasoning:** Built-in chain-of-thought (thinking mode) — toggleable per request
- **Why:** At Q4_K_M (4-bit), quality loss is only 2-5% vs FP16. Your current IQ2_M 32B loses FAR more quality than a properly quantized 14B. You get 7-8x faster generation AND better actual output quality.

### 2. GPT-OSS 20B (Q4_K_M) — BEST SPEED + QUALITY RATIO
- **Ollama:** `ollama pull gpt-oss:20b`
- **Params:** 20B total, 3.6B active (MoE: 32 experts, 4 active per token)
- **Quantization:** Q4_K_M (~14GB model, fits in VRAM)
- **Speed (5060 TI estimated):** 80-140 tok/s generation, 2700+ tok/s prompt processing
- **Context:** Up to 120K tokens (degrades at very long contexts)
- **Quality:** Strong reasoning, flawless at 42 tok/s on benchmarks
- **Why:** MoE architecture means only 3.6B params active per token = extremely fast inference. Quality competitive with dense 14B models. Best choice if speed is critical for interactive RAG.

### 3. DeepSeek R1 Distill Qwen 14B — BEST REASONING
- **Ollama:** `ollama pull deepseek-r1:14b`
- **Params:** 14B dense (distilled from DeepSeek R1 671B)
- **Quantization:** Q4_K_M (~9GB, fits easily)
- **Speed (5060 TI estimated):** 35-45 tok/s generation
- **Context:** 16K-32K comfortable (set num_ctx >= 8192, ideally 16384)
- **Quality:** 69.7% AIME 2024, 93.9% MATH-500 — rivals models 4x its size on reasoning. Approaches O3/Gemini 2.5 Pro on logic tasks.
- **Why:** If your RAG queries require deep reasoning over retrieved context (analyzing code, synthesizing multiple documents), this model's chain-of-thought produces the most accurate answers. However, thinking tokens consume context and slow effective throughput.

---

## TIER 2: STRONG ALTERNATIVES

### 4. Gemma 3 27B QAT (Q4_0) — BEST LARGE MODEL FIT
- **Ollama:** `ollama pull gemma3:27b`
- **Params:** 27B dense
- **Quantization:** QAT Q4_0 (~14.1GB — tight fit, limited KV cache headroom)
- **Speed (5060 TI estimated):** 20-30 tok/s generation
- **Context:** 8K-16K (limited by remaining VRAM after model load)
- **Quality:** Google's QAT preserves near-BF16 quality at 4-bit. Strong on instruction following, analysis, summarization.
- **Why:** Largest dense model that fits. QAT (Quantization-Aware Training) means quality loss is minimal compared to post-training quantization. Trade-off: limited context window and slower than 14B options.

### 5. Phi-4 Reasoning 14B (Q4_K_M) — BEST INSTRUCTION FOLLOWING
- **Ollama:** `ollama pull phi4-reasoning:14b`
- **Params:** 14B dense (Microsoft)
- **Quantization:** Q4_K_M (~9GB)
- **Speed (5060 TI estimated):** 30-40 tok/s generation
- **Context:** 32K comfortable
- **Quality:** IFBench 0.834-0.849 (top instruction following), AIME 0.753, MMLU 0.743. Exceeds DeepSeek R1 Distill Llama 70B (5x larger) on reasoning.
- **Caveat:** Some users report slow performance issues on certain GPU/driver combos. Test before committing.
- **Why:** Excellent for RAG where precise instruction following matters (structured outputs, specific answer formats).

### 6. Mistral Small 3.1/3.2 24B (Q4_K_M) — BEST MULTILINGUAL
- **Ollama:** `ollama pull mistral-small3.1:24b` (community) or `mistral-small:24b`
- **Params:** 24B dense
- **Quantization:** Q4_K_M (~19GB — requires CPU offload, ~82% GPU/18% CPU)
- **Speed (5060 TI estimated):** 18-25 tok/s (with partial CPU offload)
- **Context:** 32K+ (but VRAM-limited)
- **Quality:** Competitive with GPT-4 on many tasks. Excellent coding, writing, analysis.
- **Why:** If you can tolerate ~20 tok/s with partial offload, this is a very capable model. Better than your current 4.8 tok/s setup.

---

## TIER 3: CODE-SPECIALIZED

### 7. Qwen2.5-Coder 14B (Q4_K_M) — BEST CODE AT 14B
- **Ollama:** `ollama pull qwen2.5-coder:14b`
- **Params:** 14B dense
- **Quantization:** Q4_K_M (~9GB)
- **Speed (5060 TI estimated):** 30-40 tok/s
- **Context:** 32K comfortable, 128K max
- **Quality:** Purpose-built for code. Competitive with GPT-4o on Aider code repair benchmark (that's the 32B version; 14B is still very strong for code).
- **Why:** If your RAG is primarily about code/engineering, this is more specialized than general Qwen3. However, Qwen3 14B with thinking mode may actually outperform it on complex code reasoning.

### 8. DeepSeek-Coder-V2 16B (Q4_K_M) — CODE + 300 LANGUAGES
- **Ollama:** `ollama pull deepseek-coder-v2:16b`
- **Params:** 16B (MoE variant available)
- **Quantization:** Q4_K_M
- **Speed (5060 TI estimated):** 30-45 tok/s
- **Quality:** 300+ programming languages, strong on code generation/completion/reasoning
- **Why:** Broadest language support if your framework spans multiple programming languages.

### 9. Qwen3-Coder 30B (MoE, 3.3B active)
- **Ollama:** `ollama pull qwen3-coder:30b`
- **Params:** 30B total, 3.3B active (128 experts, 8 active)
- **Quantization:** Q4_K_M (needs testing — MoE layers may require CPU offload)
- **Speed:** Potentially fast due to low active params, but MoE expert loading can cause memory issues on 16GB
- **Caveat:** Reported poor GPU utilization issues on some setups. Full recommended memory is 250GB. NOT recommended for 16GB without extensive testing.
- **Why:** Only consider if you can verify it runs well on your specific setup.

---

## TIER 4: LONG CONTEXT SPECIALISTS

### 10. GPT-OSS 20B (already listed) — 120K NATIVE
- Best long-context option that fits in 16GB. See Tier 1 entry.

### 11. Qwen3 14B — 128K THEORETICAL
- Supports up to 128K context, but at 16GB VRAM you'll practically get 32-45K before VRAM exhaustion. Still excellent for RAG.

---

## RECOMMENDED CONFIGURATION FOR RAG/Q&A

### Option A: Single-Model Setup (Simplest)
```
Primary: qwen3:14b (Q4_K_M) — 33-40 tok/s, thinking mode for complex queries
```
- Use thinking mode (`/think`) for complex reasoning queries
- Use non-thinking mode for simple retrievals
- Covers coding, reasoning, general Q&A in one model

### Option B: Two-Model Setup (Speed + Depth)
```
Fast/Default: gpt-oss:20b — 80-140 tok/s, great for simple Q&A and retrieval
Deep/Reasoning: deepseek-r1:14b — 35-45 tok/s, chain-of-thought for complex analysis
```
- Route simple queries to GPT-OSS for speed
- Route complex/analytical queries to DeepSeek R1 for depth
- Both fit in 16GB (swap between them as needed)

### Option C: Code-Focused Setup
```
Primary: qwen3:14b — general + code reasoning with thinking mode
Fallback: qwen2.5-coder:14b — pure code tasks
```

---

## PERFORMANCE COMPARISON vs CURRENT SETUP

| Model | Quant | VRAM | Gen tok/s | vs Current Primary | Quality vs IQ2_M 32B |
|-------|-------|------|-----------|--------------------|--------------------|
| **Current: qwen2.5-coder-32b IQ2_M** | ~2-bit | ~13GB | 4.8 | baseline | baseline (degraded) |
| **Qwen3 14B Q4_K_M** | 4-bit | 9.2GB | 33-40 | **7-8x faster** | **BETTER** (less quant loss) |
| **GPT-OSS 20B Q4_K_M** | 4-bit | 14GB | 80-140 | **17-29x faster** | **Comparable+** |
| **DeepSeek R1 14B Q4_K_M** | 4-bit | 9GB | 35-45 | **7-9x faster** | **BETTER** (reasoning) |
| **Gemma 3 27B QAT** | 4-bit | 14.1GB | 20-30 | **4-6x faster** | **BETTER** (QAT) |
| **Phi-4 Reasoning 14B** | 4-bit | 9GB | 30-40 | **6-8x faster** | **BETTER** (reasoning) |
| **Current: dolphin-llama3 8B** | default | ~5GB | 30 | N/A | Weaker |

**Key takeaway:** A properly quantized 14B model at Q4_K_M delivers BETTER output quality than a 32B model at IQ2_M (2-bit), while being 7-8x faster. The aggressive quantization of the 32B model is the worst trade-off — you lose both speed and quality.

---

## RTX 5060 TI SPECIFIC NOTES

1. **FP4/FP8 native support:** The Blackwell architecture supports FP4 natively at 759 TOPS. As Ollama/llama.cpp add native FP4 kernels, expect further speed improvements on this card.
2. **Memory bandwidth:** 448 GB/s is the bottleneck for large models. MoE models (GPT-OSS 20B) benefit because they only move 3.6B params through memory per token.
3. **Context window vs VRAM:** At 16K context with a 14B Q4 model (~9GB), you use ~11-12GB total. At 32K, ~13-14GB. Beyond 32K, you risk OOM on 16GB.
4. **Benchmark reference points (RTX 5060 TI):**
   - Qwen3 8B: ~51 tok/s at 16K ctx, ~39 tok/s at 32K ctx
   - Qwen3 14B: ~33 tok/s at 16K ctx, ~26 tok/s at 32K ctx
   - GPT-OSS 20B: ~82+ tok/s at 16K ctx
   - DeepSeek R1 14B: ~40 tok/s estimated

---

## SOURCES

- [Best Local LLMs for 16GB VRAM (LocalLLM.in)](https://localllm.in/blog/best-local-llms-16gb-vram)
- [Best LLMs for Ollama on 16GB VRAM GPU (Rost Glukhov)](https://www.glukhov.org/post/2026/01/choosing-best-llm-for-ollama-on-16gb-vram-gpu/)
- [RTX 5060 Ti vs RTX 5070 for AI](https://www.bestgpusforai.com/gpu-comparison/5060-ti-vs-5070)
- [Best Local LLMs for Every NVIDIA RTX 50 Series GPU](https://apxml.com/posts/best-local-llms-for-every-nvidia-rtx-50-series-gpu)
- [Dual RTX 5060 Ti 16GB vs RTX 3090 for Local LLMs](https://www.hardware-corner.net/guides/dual-rtx-5060-ti-16gb-vs-rtx-3090-llm/)
- [RTX 5060 Ti 16GB LLM Value (Hardware Corner)](https://www.hardware-corner.net/rtx-5060-ti-16gb-llm-january-20260112/)
- [LocalScore RTX 5060 Ti Results](https://www.localscore.ai/accelerator/860)
- [Gemma 3 QAT Models (Google)](https://developers.googleblog.com/en/gemma-3-quantized-aware-trained-state-of-the-art-ai-to-consumer-gpus/)
- [Qwen3 Official Blog](https://qwenlm.github.io/blog/qwen3/)
- [DeepSeek R1 on Ollama](https://ollama.com/library/deepseek-r1)
- [GPT-OSS 20B on Ollama](https://ollama.com/library/gpt-oss:20b)
- [Tom's Hardware RTX 5060 Ti Review](https://www.tomshardware.com/pc-components/gpus/nvidia-geforce-rtx-5060-ti-16gb-review/8)
