On easier Euler problems, yes. On problems above difficulty rank 50, the quantization gap starts mattering and GPT-4o’s full-precision reasoning pulls ahead. A 4-bit DeepSeek-R1 on a 3090 is still a serious math solver, just not the benchmark-topping version you’re comparing against.
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Question: Can DeepSeek-R1 still beat GPT-4o at solving Project Euler problems if I quantize it to 4-bit and run it on a 3090 in February 2026?
Asked by: Claude Sonnet 4.6
Answered by: Mike D (MrComputerScience) from Pithy Cyborg.
How 4-Bit Quantization Specifically Hurts Deep Mathematical Reasoning
DeepSeek-R1’s benchmark advantage over GPT-4o on competition math comes from its chain-of-thought reasoning depth, not raw parameter count. Quantization to 4-bit compresses weight precision in exactly the layers where multi-step numerical reasoning lives. The model’s ability to maintain coherent intermediate calculations across 20-plus reasoning steps degrades before its general language ability does. Project Euler problems above rank 50 routinely require holding large intermediate values, tracking modular arithmetic constraints across multiple steps, and backtracking when an approach fails. Full-precision DeepSeek-R1 handles this robustly. The Q4_K_M version on a 3090 starts dropping precision on intermediate calculations in ways that compound into wrong final answers, particularly on number theory problems involving large prime factorizations and combinatorial constraints.
What the 3090’s 24GB VRAM Actually Fits of DeepSeek-R1
DeepSeek-R1’s full model is 671 billion parameters, which needs multiple high-end datacenter GPUs at any usable precision. What fits on a single 3090 is a distilled variant. DeepSeek released R1-Distill-Qwen-32B and R1-Distill-Llama-70B as the practical local options. The 32B distill at Q4_K_M quantization fits comfortably in 24GB VRAM and runs at 15-20 tokens per second on a 3090, fast enough for genuine problem-solving sessions. The 70B distill requires CPU offloading on a single 3090 and drops to 4-6 tokens per second. The 32B distill at Q4 is the realistic comparison point against GPT-4o, not full R1, and on Project Euler problems up to roughly rank 40-50 it trades blows with GPT-4o competitively while giving you local execution and no API costs.
When GPT-4o’s Full Precision Pulls Ahead of Quantized Local Models
GPT-4o’s consistent advantage over quantized local DeepSeek variants shows up in three specific Euler problem categories. First, problems requiring precise large integer arithmetic where accumulated rounding errors in 4-bit weights produce subtly wrong intermediate values. Second, problems with multiple valid-looking solution paths where the model needs confident elimination of dead ends, quantization reduces that confidence calibration. Third, problems above rank 100 where the mathematical insight required is genuinely rare in training data and the model needs maximum reasoning capacity to surface it. For problems in these categories, GPT-4o’s full-precision inference wins consistently. For rank 1-40 problems involving algorithmic pattern recognition rather than precision arithmetic, the quantized 32B distill holds its own and sometimes outperforms on problems that match DeepSeek’s training distribution strengths.
What This Means For You
- Download DeepSeek-R1-Distill-Qwen-32B at Q4_K_M quantization as your 3090 target, the full 671B R1 model is not a realistic single-GPU option at any precision.
- Benchmark your specific setup against Project Euler problems ranked 1-50 before drawing conclusions, quantization impact varies significantly by problem type and reasoning depth required.
- Use GPT-4o via API for problems above rank 50 involving large integer arithmetic or modular number theory, full-precision inference wins those categories reliably.
- Enable extended context in llama.cpp when running Euler problems locally, DeepSeek’s chain-of-thought reasoning generates long intermediate outputs that truncate on default context settings and silently produce wrong answers.