RTX 5060 Ti 16GB Ollama Benchmark: Llama2 13B, Mistral 7B, and DeepSeek-Coder Real Numbers (May 2026)
The headline number for the RTX 5060 Ti 16GB is 448 GB/s of GDDR7 memory bandwidth — 56% more than the 288 GB/s of the RTX 4060 Ti 16GB it replaces at the same price point. For local LLM inference, memory bandwidth is the single number that determines tokens per second. The question is whether the spec-sheet improvement translates to real-world Ollama performance on the models people actually run.
These benchmarks answer that question. Tested on May 13, 2026, with Ollama 0.23.2 and NVIDIA driver 596.36 on Windows 11, three models were benchmarked back-to-back using the same prompt: Llama2 13B (Meta’s flagship 2023 open model), Mistral 7B (the model that redefined what a 7B model could do), and DeepSeek-Coder 6.7B (the code-specialized model from DeepSeek AI). All three run at Q4_K_M quantization, the most common setting for quality-vs-size tradeoffs in Ollama.
Test Setup
| Component | Detail |
|---|---|
| GPU | NVIDIA GeForce RTX 5060 Ti 16 GB GDDR7 |
| VRAM (reported usable) | 15.9 GB |
| Memory bandwidth | 448 GB/s |
| NVIDIA driver | 596.36 |
| Ollama version | 0.23.2 |
| OS | Windows 11 |
| Inference backend | Ollama (llama.cpp) |
| Test prompt | ”Explain what is artificial intelligence in one paragraph.” |
| API mode | REST /api/generate, stream: false |
Each model was loaded cold (evicted from VRAM before the run) and measured on a single request. The 10-second gap between runs lets the GPU thermal state stabilize. VRAM usage was captured via nvidia-smi immediately after each generation completed.
Results
| Model | Parameters | Quant | Tokens/sec | VRAM used | Cold load | Total time |
|---|---|---|---|---|---|---|
| llama2:13b | 13 B | Q4_K_M | 53.44 | 11.3 GB | 9.53s | 12.05s |
| mistral:7b | 7.2 B | Q4_K_M | 90.17 | 5.9 GB | 2.39s | 4.11s |
| deepseek-coder:6.7b | 6.7 B | Q4_K_M | 101.44 | 11.6 GB | 1.69s | 2.73s |
All three models ran fully in VRAM with no CPU offload. The 5060 Ti’s 15.9 GB is sufficient for a 13B model (11.3 GB used) while leaving 4.6 GB for the KV cache and system headroom.
Model-by-Model Analysis
Llama2 13B — 53.44 tok/s
Llama2 13B was Meta’s reference model for open-weight general-purpose inference when it launched in 2023. In 2026, it sits at the lower end of the quality spectrum compared to Mistral, Qwen3, or Llama 3.x — but it is still the model that a large share of tutorials, integrations, and legacy tooling target. If you’re following documentation that says “tested on Llama2,” this is the baseline.
At 53.44 tok/s, the response experience is clearly real-time. The model generates a token roughly every 19 milliseconds — fast enough that you’re never waiting, regardless of the output length. For reference, typical human reading speed is about 4–5 words per second; at 53 tok/s you’re generating roughly 10 times faster than a reader can consume.
VRAM usage of 11.3 GB is higher than the naive parameter math suggests (a 13B Q4_K_M model weights around 7.5 GB on disk), because Ollama pre-allocates KV cache on top of the weight storage. With 15.9 GB total VRAM on the 5060 Ti, you have 4.6 GB of headroom for context — enough for conversations running into the thousands of tokens.
Cold load time of 9.53 seconds is the time to read the 7.4 GB model file from NVMe and initialize the CUDA kernels. Once loaded, the model stays resident in VRAM until evicted, so in a continuous session this cost is paid once.
Mistral 7B — 90.17 tok/s
Mistral 7B was released by Mistral AI in September 2023 and immediately set a new bar for what a 7-billion-parameter model could do. It achieves higher scores than Llama2 13B on most benchmarks despite having roughly half the parameters — a result of architectural improvements including Grouped-Query Attention (GQA) and Sliding Window Attention (SWA) that make it more parameter-efficient.
90.17 tok/s is the standout number in this benchmark. That’s the fastest of the three models tested, though DeepSeek-Coder comes close. At 90 tok/s, 200-token code completions return in just over 2 seconds. For agentic workflows where a model is called dozens of times per task, throughput like this adds up.
VRAM usage of 5.9 GB is also the lowest of the three — Mistral 7B fits comfortably on any GPU with 8 GB or more, and on the 5060 Ti leaves nearly 10 GB of free VRAM. That overhead can be used for a second simultaneous model if you’re running Ollama alongside other GPU workloads, or it simply means the model’s KV cache has enormous room to grow for long context tasks.
Cold load of 2.39 seconds is the fastest of the three, which reflects the smaller model file (4.4 GB). On a fast NVMe drive, Mistral 7B is essentially ready before you notice it’s loading.
DeepSeek-Coder 6.7B — 101.44 tok/s
DeepSeek-Coder 6.7B is a code-first model from DeepSeek AI, trained on a corpus weighted toward programming content. At 6.7 billion parameters and Q4_K_M quantization, it hits 101.44 tok/s — the fastest generation rate of the three models benchmarked, and the only one to break the 100 tok/s mark.
The throughput gap between DeepSeek-Coder and Mistral (101 vs 90 tok/s) is within the range of run-to-run variation, but the 6.7B slightly smaller parameter count (6.7B vs 7.2B for Mistral) provides marginally fewer bytes to read per token, which translates to marginally higher throughput on a bandwidth-bound GPU.
VRAM usage of 11.6 GB is significantly higher than the ~4 GB the weight file alone would suggest. This is because DeepSeek-Coder ships with a default context window of 16,384 tokens — four times Mistral’s 4,096 default. Ollama pre-allocates KV cache for the full context length at load time, which on a 6.7B model at 16K context adds approximately 6–8 GB of VRAM for the cache. The practical consequence: if you’re running DeepSeek-Coder at default settings on an 8 GB card, it won’t fit. The 5060 Ti’s 16 GB handles it without issue. You can reduce VRAM usage by setting num_ctx 4096 in an Ollama Modelfile to constrain the context window.
For code generation tasks, DeepSeek-Coder 6.7B consistently outperforms general-purpose 7B models on function completion, code explanation, and fill-in-the-middle tasks. If your use case is AI coding assistance through a tool like Continue.dev or Cline, DeepSeek-Coder is the model to benchmark against.
The Bandwidth Argument, Quantified
For memory-bound inference (single-batch generation, which is the common case for personal local AI), tokens/sec scales directly with memory bandwidth divided by model weight size:
theoretical max tok/s ≈ memory_bandwidth / model_weight_GB| Model | Weight on disk | Theoretical max (448 GB/s) | Measured | Efficiency |
|---|---|---|---|---|
| llama2:13b | 7.4 GB | ~60 tok/s | 53.44 tok/s | 89% |
| mistral:7b | 4.4 GB | ~101 tok/s | 90.17 tok/s | 89% |
| deepseek-coder:6.7b | ~3.8 GB | ~118 tok/s | 101.44 tok/s | 86% |
The 5060 Ti is running at 86–89% of theoretical bandwidth efficiency, which is excellent for a consumer GPU. The remaining ~11–14% is overhead from CUDA kernel dispatch, memory access patterns, and Windows GPU scheduling. There is no meaningful tuning to be done here — the hardware is near its ceiling.
For comparison, the same bandwidth formula on an RTX 4060 Ti (288 GB/s) gives:
- Llama2 13B: ~39 tok/s theoretical vs ~53 measured on the 5060 Ti
- Mistral 7B: ~65 tok/s theoretical vs ~90 measured on the 5060 Ti
The 5060 Ti’s GDDR7 delivers a real-world speedup of approximately 35–40% over the 4060 Ti on these models. See RTX 5060 Ti vs RTX 4060 Ti for Local AI for a fuller comparison including VRAM, power draw, and price-per-token math.
VRAM Tier Reality Check
These three models cover three distinct VRAM requirement tiers:
| Model | VRAM (default context) | Minimum GPU to run |
|---|---|---|
| mistral:7b (4K ctx) | 5.9 GB | Any 8 GB GPU |
| llama2:13b (4K ctx) | 11.3 GB | 12 GB (tight) or 16 GB |
| deepseek-coder:6.7b (16K ctx) | 11.6 GB | 16 GB |
| deepseek-coder:6.7b (4K ctx override) | ~5.5 GB | 8 GB |
The counterintuitive result is that DeepSeek-Coder 6.7B (a smaller model than Llama2 13B) consumes more VRAM at default settings because of its wider context window. This is worth knowing before you assume any 7B model will fit on an 8 GB card — context window matters as much as parameter count.
The 5060 Ti’s 16 GB handles all three comfortably. For the math on what various VRAM tiers can actually run, see How Much VRAM Do You Need for Llama Models?
Which Model for Which Use Case
Based on these benchmarks and the models’ known quality characteristics:
| Use case | Recommended model | Why |
|---|---|---|
| General chat, Q&A, writing | mistral:7b | Best quality-per-VRAM, 90 tok/s, fits 8 GB cards |
| Code completion and explanation | deepseek-coder:6.7b | Highest tok/s, purpose-trained on code |
| Long-context document analysis | llama2:13b | Wider context + higher parameter depth |
| Running alongside other GPU work | mistral:7b | 5.9 GB leaves the most headroom |
| Legacy compatibility testing | llama2:13b | Still the reference for many integrations |
The honest summary: for a fresh install targeting daily use, Mistral 7B is the starting point. It loads in under 3 seconds, uses less than 6 GB of VRAM, and outperforms Llama2 13B on most generation quality benchmarks while running at nearly 2× the throughput. DeepSeek-Coder is the swap-in for any session involving code; it’s effectively a Mistral-class model fine-tuned for programming tasks.
Honest Take: What the 5060 Ti Actually Buys You
The results above show the 5060 Ti delivering 90+ tok/s on 7B models and 53 tok/s on a 13B model — numbers that feel fast in real use. Running Mistral 7B through Open WebUI, every response completes before you finish reading the first sentence.
The card’s limits are also clear: 70B models are out (requires 24+ GB VRAM), and anything in the 30–40B range would require partial CPU offload with significant throughput penalties. If 70B quality is your target, the used RTX 3090 24GB actually has more bandwidth at 936 GB/s and 24 GB VRAM — but at roughly $680+ used, 350W TDP (vs the 5060 Ti’s 180W), and the reliability risk of a used card. The 5060 Ti’s case is efficiency and warranty, not raw LLM throughput ceiling. See RTX 5060 Ti 16GB vs Used RTX 3090: 3-Year Total Cost for the full TCO breakdown.
For anyone running a home AI setup targeting 7B to 13B models — which is the practical sweet spot for a machine you also use for other things — the RTX 5060 Ti 16GB hits the throughput ceiling for these model sizes with GDDR7 bandwidth and fits all three models into VRAM without offload or compromise.
Sources
- NVIDIA GeForce RTX 5060 Ti Specifications — NVIDIA Official
- Ollama v0.23.2 Release Notes — GitHub
- Llama 2: Open Foundation and Fine-Tuned Chat Models — Meta AI
- Mistral 7B — Mistral AI
- DeepSeek-Coder: Let the Code Write Itself — DeepSeek AI
- RTX 5060 Ti vs RTX 4060 Ti for Local AI — RunAIHome
- How Much VRAM Do You Need for Llama Models? — RunAIHome
Last updated May 13, 2026. Benchmarks run on Ollama 0.23.2, NVIDIA driver 596.36, Windows 11. Performance varies with driver version, OS, and Ollama release.