SKU: 83452348711

TOTA | 150 grain broadhead | 1-inch solid | fixed blade

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Description

TOTA | 150 grain broadhead | 1-inch solid | fixed blade4 Blade 1 Inch Solid Broadhead 3 Broadheads Per Pack One goal: Complete devastation. This 4 blade fixed blade broadhead is built for field point accuracy, bone crushing durability, and straight up lethal performance. Fully machined right here in the USA, its razor sharp out of the box and ready to hunt. No tuning, no nonsense. Field point accuracy, insane durability. Durable, single piece construction no weak spots. Easy to resharpen, built to fill

4 Blade 1-Inch Solid Broadhead – 3 Broadheads Per Pack

 

One goal: Complete devastation.

 

This 4 blade fixed blade broadhead is built for field-point accuracy, bone-crushing durability, and straight-up lethal performance. Fully machined right here in the USA, it’s razor-sharp out of the box and ready to hunt. No tuning, no nonsense.

 

Field point accuracy, insane durability.

 

Durable, single-piece construction – no weak spots.

 

Easy to resharpen, built to fill your freezer or trophy room.

 

Satisfaction Guaranteed – No Ifs, Ands, or Buts.

 

Our Recommendation: Ideal for medium to advanced bowhunters looking for reliable performance.

For Hunters: Perfect for deer, antelope, elk, and other medium to large game, especially for close to medium range shots (20–60 yards).

 

Not sure which broadhead is right for you? Take our broadhead quiz

 

Tech Specs – 4 Blade 1-Inch Solid Broadhead

Grain Weight: 100 grain, 125 grain, 150 grain

 

Cutting Diameter: 1″

 

Blade Count: 4

 

Blade Thickness: 0.040″

 

Blade Material: Heat-treated high-carbon steel

 

Ferrule Material: Heat-treated high-carbon steel

 

Tip Style: Tanto

 

Deployment: N/A

 

Blades Replaceable:  No

 

Blades Angle: 22.5 degrees per side for a 45 degree blade

 

Made In: USA

 

Pack Size: 3 broadheads

 

These premium 1-inch solid 4-blade fixed broadheads are built for archers who want field-point accuracy with fixed-blade reliability. With a compact 1-inch cutting diameter and precision venting, these heads fly true even at high speeds and in windy conditions.


Machined from a single piece of high-carbon steel and heat-treated for maximum durability, each broadhead is built to withstand bone impact and repeated use. The four-blade design creates a perfectly square wound channel for increased blood loss and easier tracking, while the razor-sharp cut-on-contact blades deliver immediate penetration.


Made in the USA and spin-tested to ensure perfect flight, these heads are available in 100, 125 and 150 grains — ideal for bowhunters who demand toughness, pass-throughs, and consistent, no-fail performance.

Features

 

 

Field-Point Accuracy – A solid blade design and compact 1-inch profile allow these fixed blades to fly like field points, even at high arrow speeds and long distances.

 

One-Piece Steel Construction – Machined from a single bar of high-carbon steel and fully heat-treated for unmatched durability and impact resistance.

 

4-Blade Cut-on-Contact Design – Creates a true square wound channel for increased blood loss, better penetration, and faster recoveries.

 

Razor Sharp at a 45° Total Blade Angle – Each blade is ground to 22.5° per side (45° total) for an ideal balance of sharpness and edge strength.

 

Tanto Tip for Bone-Crushing Penetration – Reinforced tip design punches through hide, muscle, and bone without folding or curling.

 

No Moving Parts, No Failure Points – Solid fixed-blade design means zero deployment worries, zero rubber bands, and 100% reliability.

 

Available in 100, 125, and 150 Grains – Tuned for a wide range of bow setups, from traditional to high-speed compound.

 

Proudly Made in the USA – Fully machined, heat-treated, and packaged in the USA.

 

FAQ

 

Q: Are the blades replaceable?

A: No. These are one-piece fixed broadheads machined from a single bar of high-carbon steel, so the blades are not removable or replaceable.

 

Q: Will these fly like my field points?

A: Yes—when your bow is properly tuned and arrows are spined correctly, these heads fly like field points. The 1-inch cutting diameter helps reduce wind drag and planing.

 

Q: What grain weights are available?

A: They are available in 100, 125, and 150 grain options.

 

Q: What comes in each pack?

A: Each pack includes 3 broadheads.

 

Q: Can these be used for whitetail deer and similar game?

A: Absolutely. These are ideal for whitetail, mule deer, antelope, hogs, and other small to medium game.

 

Q: Do I need a certain draw weight or arrow speed for these to work properly?

A: No minimum draw weight or speed is required. They perform well out of modern compound bows, traditional bows, and crossbows (as long as the bow is tuned and arrows are properly matched).

 

Q: Why a 4-blade and not a 2-blade?

A: The 4-blade design creates a true square wound channel, increasing blood loss and leaving easier-to-follow blood trails compared to 2-blade designs.

 

Q: Are these made in the USA?

A: Yes. These broadheads are fully machined, heat-treated, and packaged in the USA.

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SKU: 83452348711

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Par
Lake Worth, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
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Verified Purchase
Richard Hackathorn
Cuba, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
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Verified Purchase
Amazon Customer
Chelsea, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
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Kindle Customer
Louisville, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
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Verified Purchase
Tommy Jonsson
Boise, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026

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