DeepSeek-R1 14B FP16 VRAM Requirement and Ollama Model Size
DeepSeek-R1 14B FP16 needs 22GB minimum VRAM, 34GB optimal VRAM, and 64GB+ system RAM for a safer Ollama run. Popular Ollama model family: DeepSeek-R1. Caveat: Estimated values are placeholders unless marked measured..
Hardware Snapshot
| Family | DeepSeek-R1 |
|---|---|
| Scenario | reasoning |
| License scope | open-source |
| Quantization | FP16 |
| VRAM minimum | 22GB |
| VRAM optimal | 34GB |
| Best local GPU | RTX 6000 Ada 48GB |
| Cloud fallback | A100 80GB |
| Updated | 2026-02-24 |
| Data status | Verified by Real Hardware |
| Ollama source | Library reference (verified: 2026-02-24) |
| Ollama tag | deepseek-r1:14b |
| Category | reasoning |
Benchmark Anchors
| Hardware | Expected tok/s |
|---|---|
| RTX 3090 24GB | 11.6 |
| RTX 4090 24GB | 15.7 |
| A100 80GB | 27.8 |
Quick Answers
- How much VRAM does DeepSeek-R1 14B FP16 need?
- DeepSeek-R1 14B FP16 needs about 22GB minimum VRAM and 34GB optimal VRAM for a safer local run target.
- Can DeepSeek-R1 14B FP16 run on an RTX 3090 24GB?
- It may load on an RTX 3090 24GB because the minimum estimate is 22GB, but the safer target is 34GB. Expect tighter context or lower throughput.
- What is the Ollama command for DeepSeek-R1 14B FP16?
- Use ollama run deepseek-r1:14b. Check the Ollama tag deepseek-r1:14b before running.
- What is the approximate Ollama model size for DeepSeek-R1 14B FP16?
- DeepSeek-R1 14B FP16 uses the deepseek-r1:14b Ollama tag. Treat the FP16 14 profile as smaller than the runtime VRAM budget; use 22GB minimum VRAM and 34GB optimal VRAM as the safer sizing numbers.
- How much system RAM does DeepSeek-R1 14B FP16 need in Ollama?
- Plan for at least 64GB system RAM alongside 22GB minimum VRAM and 34GB optimal VRAM. More RAM helps if layers spill to CPU or if you run multiple models.
- What is the RAM vs VRAM requirement for DeepSeek-R1 14B FP16?
- DeepSeek-R1 14B FP16 should be sized by GPU VRAM first: 22GB minimum and 34GB optimal. System RAM should be at least 64GB so Ollama has room for CPU-side layers, context, and the surrounding app process.
- What is DeepSeek-R1 14B FP16 VRAM usage on an RTX 3090?
- DeepSeek-R1 14B FP16 is borderline for an RTX 3090 24GB target. The page estimates 22GB minimum and 34GB optimal VRAM, with spill risk marked as high without a larger GPU or cloud.
- Which LocalVRAM profiles share the deepseek-r1:14b Ollama tag?
- LocalVRAM tracks these profiles for the same Ollama tag or model family: DeepSeek-R1 14B FP16 (FP16, 22GB min/34GB optimal); DeepSeek-R1 14B Q4 (Q4, 10GB min/20GB optimal); DeepSeek-R1 14B Q5 (Q5, 12GB min/22GB optimal); DeepSeek-R1 14B Q8 (Q8, 16GB min/26GB optimal). Use the table on this page to compare quantization and VRAM targets before choosing a run command.
- Is DeepSeek-R1 14B FP16 benchmark data measured or estimated?
- DeepSeek-R1 14B FP16 has a verified RTX 3090 result of 78.909 tokens/s from LocalVRAM benchmark data.
Ollama Model Size and RAM Notes
| Ollama tag | deepseek-r1:14b |
|---|---|
| Run command | ollama run deepseek-r1:14b |
| Model size guidance | FP16 quantization, 14 profile; size runtime by VRAM, not download size alone |
| Runtime VRAM target | 22GB minimum, 34GB optimal |
| System RAM planning | 64GB or more when running this tag locally with Ollama |
Download size alone is not a safe runtime budget. Runtime memory also includes KV cache, context length, and GPU layer placement.
Ollama RAM vs VRAM Requirements
| GPU VRAM usage estimate | 22GB minimum / 34GB optimal |
|---|---|
| System RAM requirement | 64GB or more for Ollama runtime headroom |
| RTX 3090 24GB fit | Borderline |
| CPU spill risk | High without larger GPU or cloud |
| Context warning | Longer context increases KV cache memory, so real VRAM usage can exceed short-prompt estimates. |
For Ollama, VRAM is the first fit check. System RAM matters when layers spill to CPU, when context grows, or when the local app stack runs beside the model.
Available Tag Variants and VRAM Targets
| Profile | Quantization | VRAM target | Ollama tag |
|---|---|---|---|
| DeepSeek-R1 14B FP16 | FP16 | 22GB min / 34GB optimal | deepseek-r1:14b |
| DeepSeek-R1 14B Q4 | Q4 | 10GB min / 20GB optimal | deepseek-r1:14b |
| DeepSeek-R1 14B Q5 | Q5 | 12GB min / 22GB optimal | deepseek-r1:14b |
| DeepSeek-R1 14B Q8 | Q8 | 16GB min / 26GB optimal | deepseek-r1:14b |
Ollama library tags often group multiple quantization choices under one model family. LocalVRAM separates them into VRAM profiles so you can pick the right local target.
Real Hardware Benchmark (RTX 3090)
| Tokens/s | 78.909 |
|---|---|
| Latency | 2067 ms |
| Prompt tokens | 24 |
| Eval tokens | 128 |
| Test time | 2026-08-19T03:08:42Z |
| GPU model | NVIDIA GeForce RTX 3090 |
Verified by real hardware.
Performance Curve
Reference anchors are baseline estimates. Measured RTX 3090 data is overlaid when available.
Best Hardware for DeepSeek-R1 14B FP16
- Local run: RTX 3090 (24GB) (Check latest deal) for around 78.909 tok/s on this profile.
- Cloud run: RunPod A100 80GB , about 0.4x the local 3090 speed anchor.
- Alternative cloud: Vast.ai options for flexible spot pricing.
Local vs Cloud Cost Hint
| Mode | 40h / month | 120h / month |
|---|---|---|
| Local power only (3090 baseline) | $2.24 | $6.72 |
| A100 80GB | $78 | $234 |
Related Model Profiles
- DeepSeek-R1 14B Q4 10GB min, 20GB optimal
- DeepSeek-R1 14B Q5 12GB min, 22GB optimal
- DeepSeek-R1 14B Q8 16GB min, 26GB optimal
ollama run deepseek-r1:14b More reasoning models More 14b-class models Benchmark changelog Submit your test result Run on RunPod Try Vast.ai We may earn a commission if you click links on this page.